From f036d9903e14b2b15b3bc21a921bcb86db1748c3 Mon Sep 17 00:00:00 2001 From: Rajdip Das Date: Thu, 30 Mar 2023 13:19:41 +0530 Subject: [PATCH] fix some problem --- ...-bluebook-bulldozer-price-regression.ipynb | 985 +++++++++++------- 1 file changed, 635 insertions(+), 350 deletions(-) diff --git a/section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression.ipynb b/section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression.ipynb index 2ae496760..aeb326626 100644 --- a/section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression.ipynb +++ b/section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression.ipynb @@ -103,8 +103,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/daniel/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (13,39,40,41) have mixed types.Specify dtype option on import or set low_memory=False.\n", - " interactivity=interactivity, compiler=compiler, result=result)\n" + "C:\\Users\\Lenovo\\AppData\\Local\\Temp\\ipykernel_9792\\2536095483.py:2: DtypeWarning: Columns (13,39,40,41) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df = pd.read_csv(\"../data/bluebook-for-bulldozers/TrainAndValid.csv\")\n" ] } ], @@ -198,7 +198,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -207,14 +207,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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u8vLytGjRIm3fvl1Op1PDhw/XiRMnzJqsrCytWbNG+fn5Ki4u1smTJ5WamqqGhgazJj09XR6PRwUFBSooKJDH45Hb7TbHGxoalJKSolOnTqm4uFj5+flatWqVsrOzzZqamhoNHz5cLpdL27dv1/PPP6/58+crLy/vKswMAAAIRDajlSy52Gw2rVmzRvfee6+kc6tVLpdLWVlZmjZtmqRzq1OxsbGaM2eOxo8fL5/PpxtvvFGvvfaaHnjgAUnS4cOHFRcXp3feeUfJycnau3evevbsqZKSEvXr10+SVFJSosTERH3yySfq3r273n33XaWmpqqiokIul0uSlJ+fr7Fjx6q6ulqRkZFavHixZsyYoSNHjshut0uSZs+ereeff14HDx6UzWa7rOOsqamRw+GQz+dTZGSklVOoLtPXWro/XNzns1NaugUAwLfscj+/W+05Vvv371dVVZWSkpLMbXa7XYMGDdLmzZslSaWlpaqvr/ercblcio+PN2u2bNkih8NhhipJ6t+/vxwOh19NfHy8GaokKTk5WbW1tSotLTVrBg0aZIaq8zWHDx/W559//pXHUVtbq5qaGr8HAABom1ptsKqqqpIkxcbG+m2PjY01x6qqqhQaGqqoqKhL1sTExDTZf0xMjF/Nhe8TFRWl0NDQS9acf36+5mJyc3PNc7scDofi4uIufeAAACBgtdpgdd6FX7EZhvG1X7tdWHOxeitqzn+Leql+ZsyYIZ/PZz4qKiou2TsAAAhcrTZYOZ1OSU1Xg6qrq82VIqfTqbq6Onm93kvWHDlypMn+jx496ldz4ft4vV7V19dfsqa6ulpS01W1L7Pb7YqMjPR7AACAtqnVBquuXbvK6XSqqKjI3FZXV6eNGzdqwIABkqSEhASFhIT41VRWVqqsrMysSUxMlM/n07Zt28yarVu3yufz+dWUlZWpsrLSrCksLJTdbldCQoJZs2nTJr9LMBQWFsrlcqlLly7WTwAAAAg4LRqsTp48KY/HI4/HI+ncCesej0fl5eWy2WzKyspSTk6O1qxZo7KyMo0dO1bt2rVTenq6JMnhcGjcuHHKzs7W+vXrtWvXLo0ePVq9e/fWsGHDJEk9evTQiBEjlJGRoZKSEpWUlCgjI0Opqanq3r27JCkpKUk9e/aU2+3Wrl27tH79ek2dOlUZGRnmClN6errsdrvGjh2rsrIyrVmzRjk5OZoyZcpl/yIQAAC0bcEt+eY7duzQkCFDzOdTpkyRJI0ZM0bLly/XU089pdOnT2vChAnyer3q16+fCgsLFRERYb5mwYIFCg4O1qhRo3T69GkNHTpUy5cvV1BQkFmzcuVKZWZmmr8eTEtL87t2VlBQkNauXasJEyZo4MCBCgsLU3p6uubPn2/WOBwOFRUV6YknnlDfvn0VFRWlKVOmmD0DAAC0mutYXSu4jlXg4zpWAHDtCfjrWAEAAAQaghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYJFmBav9+/db3QcAAEDAa1aw+s53vqMhQ4ZoxYoVOnPmjNU9AQAABKRmBasPP/xQffr0UXZ2tpxOp8aPH69t27ZZ3RsAAEBAaVawio+PV15eng4dOqRly5apqqpKd911l3r16qW8vDwdPXrU6j4BAABavW908npwcLDuu+8+/fGPf9ScOXP097//XVOnTlXHjh31yCOPqLKy0qo+AQAAWr1vFKx27NihCRMm6KabblJeXp6mTp2qv//973r//fd16NAh/fjHP7aqTwAAgFYvuDkvysvL07Jly/Tpp5/qnnvu0auvvqp77rlH1113Lqd17dpVS5Ys0W233WZpswAAAK1Zs4LV4sWL9eijj+qnP/2pnE7nRWs6deqkl19++Rs1BwAAEEiaFaw+++yzr60JDQ3VmDFjmrN7AACAgNSsc6yWLVumP/3pT022/+lPf9Irr7zyjZsCAAAIRM0KVrNnz1Z0dHST7TExMcrJyfnGTQEAAASiZgWrAwcOqGvXrk22d+7cWeXl5d+4KQAAgEDUrGAVExOjjz76qMn2Dz/8UB06dPjGTQEAAASiZgWrBx98UJmZmdqwYYMaGhrU0NCg999/X5MnT9aDDz5odY8AAAABoVm/Cvztb3+rAwcOaOjQoQoOPreLxsZGPfLII5xjBQAArlnNClahoaF688039V//9V/68MMPFRYWpt69e6tz585W9wcAABAwmhWszrv11lt16623WtULAABAQGtWsGpoaNDy5cu1fv16VVdXq7Gx0W/8/ffft6Q5AACAQNKsYDV58mQtX75cKSkpio+Pl81ms7ovAACAgNOsYJWfn68//vGPuueee6zuBwAAIGA163ILoaGh+s53vmN1LwAAAAGtWcEqOztbv/vd72QYhtX9AAAABKxmfRVYXFysDRs26N1331WvXr0UEhLiN7569WpLmgMAAAgkzQpWN9xwg+677z6rewEAAAhozQpWy5Yts7oPAACAgNesc6wk6ezZs1q3bp2WLFmiEydOSJIOHz6skydPWtYcAABAIGnWitWBAwc0YsQIlZeXq7a2VsOHD1dERITmzp2rM2fO6MUXX7S6TwAAgFavWStWkydPVt++feX1ehUWFmZuv++++7R+/XrLmjt79qx++ctfqmvXrgoLC9Mtt9yiWbNm+V3p3TAMzZw5Uy6XS2FhYRo8eLD27Nnjt5/a2lpNmjRJ0dHRCg8PV1pamg4ePOhX4/V65Xa75XA45HA45Ha7dfz4cb+a8vJyjRw5UuHh4YqOjlZmZqbq6uosO14AABDYmhWsiouL9ctf/lKhoaF+2zt37qxDhw5Z0pgkzZkzRy+++KIWLVqkvXv3au7cuZo3b56ef/55s2bu3LnKy8vTokWLtH37djmdTg0fPtz8elKSsrKytGbNGuXn56u4uFgnT55UamqqGhoazJr09HR5PB4VFBSooKBAHo9HbrfbHG9oaFBKSopOnTql4uJi5efna9WqVcrOzrbseAEAQGBr1leBjY2NfqHkvIMHDyoiIuIbN3Xeli1b9OMf/1gpKSmSpC5duuiNN97Qjh07JJ1brVq4cKGefvpp3X///ZKkV155RbGxsXr99dc1fvx4+Xw+vfzyy3rttdc0bNgwSdKKFSsUFxendevWKTk5WXv37lVBQYFKSkrUr18/SdLSpUuVmJioTz/9VN27d1dhYaE+/vhjVVRUyOVySZKee+45jR07Vs8++6wiIyMtO24AABCYmrViNXz4cC1cuNB8brPZdPLkSf3mN7+x9DY3d911l9avX699+/ZJkj788EMVFxeb77F//35VVVUpKSnJfI3dbtegQYO0efNmSVJpaanq6+v9alwul+Lj482aLVu2yOFwmKFKkvr37y+Hw+FXEx8fb4YqSUpOTlZtba1KS0u/8hhqa2tVU1Pj9wAAAG1Ts1asFixYoCFDhqhnz546c+aM0tPT9dlnnyk6OlpvvPGGZc1NmzZNPp9Pt912m4KCgtTQ0KBnn31WDz30kCSpqqpKkhQbG+v3utjYWB04cMCsCQ0NVVRUVJOa86+vqqpSTExMk/ePiYnxq7nwfaKiohQaGmrWXExubq6eeeaZKzlsAAAQoJoVrFwulzwej9544w3t3LlTjY2NGjdunB5++GG/k9m/qTfffFMrVqzQ66+/rl69esnj8SgrK0sul0tjxowx62w2m9/rDMNosu1CF9ZcrL45NReaMWOGpkyZYj6vqalRXFzcJXsDAACBqVnBSpLCwsL06KOP6tFHH7WyHz9PPvmkpk+frgcffFCS1Lt3bx04cEC5ubkaM2aMnE6npHOrSTfddJP5uurqanN1yel0qq6uTl6v12/Vqrq6WgMGDDBrjhw50uT9jx496refrVu3+o17vV7V19c3Wcn6MrvdLrvd3pzDBwAAAaZZwerVV1+95PgjjzzSrGYu9MUXX+i66/xPAwsKCjIvt9C1a1c5nU4VFRWpT58+kqS6ujpt3LhRc+bMkSQlJCQoJCRERUVFGjVqlCSpsrJSZWVlmjt3riQpMTFRPp9P27Zt05133ilJ2rp1q3w+nxm+EhMT9eyzz6qystIMcYWFhbLb7UpISLDkeAEAQGBrVrCaPHmy3/P6+np98cUXCg0NVbt27SwLViNHjtSzzz6rTp06qVevXtq1a5fy8vLMVTKbzaasrCzl5OSoW7du6tatm3JyctSuXTulp6dLkhwOh8aNG6fs7Gx16NBB7du319SpU9W7d2/zV4I9evTQiBEjlJGRoSVLlkiSHnvsMaWmpqp79+6SpKSkJPXs2VNut1vz5s3TsWPHNHXqVGVkZPCLQAAAIKmZwcrr9TbZ9tlnn+nnP/+5nnzyyW/c1HnPP/+8fvWrX2nChAmqrq6Wy+XS+PHj9etf/9qseeqpp3T69GlNmDBBXq9X/fr1U2Fhod9lHxYsWKDg4GCNGjVKp0+f1tChQ7V8+XIFBQWZNStXrlRmZqb568G0tDQtWrTIHA8KCtLatWs1YcIEDRw4UGFhYUpPT9f8+fMtO14AABDYbIZhGFbtbMeOHRo9erQ++eQTq3bZ5tTU1MjhcMjn81m+0tVl+lpL94eL+3x2Sku3AAD4ll3u53ezb8J8MUFBQTp8+LCVuwQAAAgYzfoq8O233/Z7bhiGKisrtWjRIg0cONCSxgAAAAJNs4LVvffe6/fcZrPpxhtv1A9/+EM999xzVvQFAAAQcJp9r0AAAAD4s/QcKwAAgGtZs1asvnyLlq+Tl5fXnLcAAAAIOM0KVrt27dLOnTt19uxZ8wKa+/btU1BQkO644w6z7uvu1wcAANCWNCtYjRw5UhEREXrllVfM++95vV799Kc/1d13363s7GxLmwQAAAgEzTrH6rnnnlNubq7fTY2joqL029/+ll8FAgCAa1azglVNTY2OHDnSZHt1dbVOnDjxjZsCAAAIRM0KVvfdd59++tOf6s9//rMOHjyogwcP6s9//rPGjRun+++/3+oeAQAAAkKzzrF68cUXNXXqVI0ePVr19fXndhQcrHHjxmnevHmWNggAABAomhWs2rVrpxdeeEHz5s3T3//+dxmGoe985zsKDw+3uj8AAICA8Y0uEFpZWanKykrdeuutCg8Pl2EYVvUFAAAQcJoVrP71r39p6NChuvXWW3XPPfeosrJSkvSzn/2MSy0AAIBrVrOC1S9+8QuFhISovLxc7dq1M7c/8MADKigosKw5AACAQNKsc6wKCwv13nvvqWPHjn7bu3XrpgMHDljSGAAAQKBp1orVqVOn/FaqzvvnP/8pu93+jZsCAAAIRM0KVj/4wQ/06quvms9tNpsaGxs1b948DRkyxLLmAAAAAkmzvgqcN2+eBg8erB07dqiurk5PPfWU9uzZo2PHjumvf/2r1T0CAAAEhGatWPXs2VMfffSR7rzzTg0fPlynTp3S/fffr127dunf//3fre4RAAAgIFzxilV9fb2SkpK0ZMkSPfPMM1ejJwAAgIB0xStWISEhKisrk81muxr9AAAABKxmfRX4yCOP6OWXX7a6FwAAgIDWrJPX6+rq9Ic//EFFRUXq27dvk3sE5uXlWdIcAABAILmiYPWPf/xDXbp0UVlZme644w5J0r59+/xq+IoQAABcq64oWHXr1k2VlZXasGGDpHO3sPn973+v2NjYq9IcAABAILmic6wMw/B7/u677+rUqVOWNgQAABComnXy+nkXBi0AAIBr2RUFK5vN1uQcKs6pAgAAOOeKzrEyDENjx441b7R85swZPf74401+Fbh69WrrOgQAAAgQVxSsxowZ4/d89OjRljYDAAAQyK4oWC1btuxq9QEAABDwvtHJ6wAAAPj/EawAAAAs0uqD1aFDhzR69Gh16NBB7dq10/e+9z2Vlpaa44ZhaObMmXK5XAoLC9PgwYO1Z88ev33U1tZq0qRJio6OVnh4uNLS0nTw4EG/Gq/XK7fbLYfDIYfDIbfbrePHj/vVlJeXa+TIkQoPD1d0dLQyMzNVV1d31Y4dAAAEllYdrLxerwYOHKiQkBC9++67+vjjj/Xcc8/phhtuMGvmzp2rvLw8LVq0SNu3b5fT6dTw4cN14sQJsyYrK0tr1qxRfn6+iouLdfLkSaWmpqqhocGsSU9Pl8fjUUFBgQoKCuTxeOR2u83xhoYGpaSk6NSpUyouLlZ+fr5WrVql7Ozsb2UuAABA62czWvFVPqdPn66//vWv+uCDDy46bhiGXC6XsrKyNG3aNEnnVqdiY2M1Z84cjR8/Xj6fTzfeeKNee+01PfDAA5Kkw4cPKy4uTu+8846Sk5O1d+9e9ezZUyUlJerXr58kqaSkRImJifrkk0/UvXt3vfvuu0pNTVVFRYVcLpckKT8/X2PHjlV1dbUiIyMv65hqamrkcDjk8/ku+zWXq8v0tZbuDxf3+eyUlm4BAPAtu9zP71a9YvX222+rb9+++slPfqKYmBj16dNHS5cuNcf379+vqqoqJSUlmdvsdrsGDRqkzZs3S5JKS0tVX1/vV+NyuRQfH2/WbNmyRQ6HwwxVktS/f385HA6/mvj4eDNUSVJycrJqa2v9vpq8UG1trWpqavweAACgbWrVweof//iHFi9erG7duum9997T448/rszMTL366quSpKqqKklqchPo2NhYc6yqqkqhoaGKioq6ZE1MTEyT94+JifGrufB9oqKiFBoaatZcTG5urnnelsPhUFxc3JVMAQAACCCtOlg1NjbqjjvuUE5Ojvr06aPx48crIyNDixcv9qu78LY6hmF87a12Lqy5WH1zai40Y8YM+Xw+81FRUXHJvgAAQOBq1cHqpptuUs+ePf229ejRQ+Xl5ZIkp9MpSU1WjKqrq83VJafTqbq6Onm93kvWHDlypMn7Hz161K/mwvfxer2qr69vspL1ZXa7XZGRkX4PAADQNrXqYDVw4EB9+umnftv27dunzp07S5K6du0qp9OpoqIic7yurk4bN27UgAEDJEkJCQkKCQnxq6msrFRZWZlZk5iYKJ/Pp23btpk1W7dulc/n86spKytTZWWlWVNYWCi73a6EhASLjxwAAASiK7qlzbftF7/4hQYMGKCcnByNGjVK27Zt00svvaSXXnpJ0rmv5rKyspSTk6Nu3bqpW7duysnJUbt27ZSeni5JcjgcGjdunLKzs9WhQwe1b99eU6dOVe/evTVs2DBJ51bBRowYoYyMDC1ZskSS9Nhjjyk1NVXdu3eXJCUlJalnz55yu92aN2+ejh07pqlTpyojI4NVKAAAIKmVB6vvf//7WrNmjWbMmKFZs2apa9euWrhwoR5++GGz5qmnntLp06c1YcIEeb1e9evXT4WFhYqIiDBrFixYoODgYI0aNUqnT5/W0KFDtXz5cgUFBZk1K1euVGZmpvnrwbS0NC1atMgcDwoK0tq1azVhwgQNHDhQYWFhSk9P1/z587+FmUBrEoiXteASEQDw7WjV17Fqi7iOFVoCwQoAvpk2cR0rAACAQEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAiwRUsMrNzZXNZlNWVpa5zTAMzZw5Uy6XS2FhYRo8eLD27Nnj97ra2lpNmjRJ0dHRCg8PV1pamg4ePOhX4/V65Xa75XA45HA45Ha7dfz4cb+a8vJyjRw5UuHh4YqOjlZmZqbq6uqu1uECAIAAEzDBavv27XrppZf03e9+12/73LlzlZeXp0WLFmn79u1yOp0aPny4Tpw4YdZkZWVpzZo1ys/PV3FxsU6ePKnU1FQ1NDSYNenp6fJ4PCooKFBBQYE8Ho/cbrc53tDQoJSUFJ06dUrFxcXKz8/XqlWrlJ2dffUPHgAABISACFYnT57Uww8/rKVLlyoqKsrcbhiGFi5cqKefflr333+/4uPj9corr+iLL77Q66+/Lkny+Xx6+eWX9dxzz2nYsGHq06ePVqxYod27d2vdunWSpL1796qgoEB/+MMflJiYqMTERC1dulR/+ctf9Omnn0qSCgsL9fHHH2vFihXq06ePhg0bpueee05Lly5VTU3Ntz8pAACg1QmIYPXEE08oJSVFw4YN89u+f/9+VVVVKSkpydxmt9s1aNAgbd68WZJUWlqq+vp6vxqXy6X4+HizZsuWLXI4HOrXr59Z079/fzkcDr+a+Ph4uVwusyY5OVm1tbUqLS39yt5ra2tVU1Pj9wAAAG1TcEs38HXy8/O1c+dObd++vclYVVWVJCk2NtZve2xsrA4cOGDWhIaG+q10na85//qqqirFxMQ02X9MTIxfzYXvExUVpdDQULPmYnJzc/XMM8983WECAIA2oFWvWFVUVGjy5MlasWKFrr/++q+ss9lsfs8Nw2iy7UIX1lysvjk1F5oxY4Z8Pp/5qKiouGRfAAAgcLXqYFVaWqrq6molJCQoODhYwcHB2rhxo37/+98rODjYXEG6cMWourraHHM6naqrq5PX671kzZEjR5q8/9GjR/1qLnwfr9er+vr6JitZX2a32xUZGen3AAAAbVOrDlZDhw7V7t275fF4zEffvn318MMPy+Px6JZbbpHT6VRRUZH5mrq6Om3cuFEDBgyQJCUkJCgkJMSvprKyUmVlZWZNYmKifD6ftm3bZtZs3bpVPp/Pr6asrEyVlZVmTWFhoex2uxISEq7qPAAAgMDQqs+xioiIUHx8vN+28PBwdejQwdyelZWlnJwcdevWTd26dVNOTo7atWun9PR0SZLD4dC4ceOUnZ2tDh06qH379po6dap69+5tngzfo0cPjRgxQhkZGVqyZIkk6bHHHlNqaqq6d+8uSUpKSlLPnj3ldrs1b948HTt2TFOnTlVGRgarUAAAQFIrD1aX46mnntLp06c1YcIEeb1e9evXT4WFhYqIiDBrFixYoODgYI0aNUqnT5/W0KFDtXz5cgUFBZk1K1euVGZmpvnrwbS0NC1atMgcDwoK0tq1azVhwgQNHDhQYWFhSk9P1/z587+9gwUAAK2azTAMo6WbuJbU1NTI4XDI5/NZvtLVZfpaS/eHtuPz2Skt3QIABLTL/fxu1edYAQAABBKCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFgn4mzAD+HqBeB9J7m8IIBCxYgUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYJHglm4AAC6my/S1Ld1Cs3w+O6WlWwDQglixAgAAsAjBCgAAwCKtOljl5ubq+9//viIiIhQTE6N7771Xn376qV+NYRiaOXOmXC6XwsLCNHjwYO3Zs8evpra2VpMmTVJ0dLTCw8OVlpamgwcP+tV4vV653W45HA45HA653W4dP37cr6a8vFwjR45UeHi4oqOjlZmZqbq6uqty7AAAIPC06mC1ceNGPfHEEyopKVFRUZHOnj2rpKQknTp1yqyZO3eu8vLytGjRIm3fvl1Op1PDhw/XiRMnzJqsrCytWbNG+fn5Ki4u1smTJ5WamqqGhgazJj09XR6PRwUFBSooKJDH45Hb7TbHGxoalJKSolOnTqm4uFj5+flatWqVsrOzv53JAAAArZ7NMAyjpZu4XEePHlVMTIw2btyoH/zgBzIMQy6XS1lZWZo2bZqkc6tTsbGxmjNnjsaPHy+fz6cbb7xRr732mh544AFJ0uHDhxUXF6d33nlHycnJ2rt3r3r27KmSkhL169dPklRSUqLExER98skn6t69u959912lpqaqoqJCLpdLkpSfn6+xY8equrpakZGRl3UMNTU1cjgc8vl8l/2ayxWoJ/sCbQknrwNt0+V+frfqFasL+Xw+SVL79u0lSfv371dVVZWSkpLMGrvdrkGDBmnz5s2SpNLSUtXX1/vVuFwuxcfHmzVbtmyRw+EwQ5Uk9e/fXw6Hw68mPj7eDFWSlJycrNraWpWWln5lz7W1taqpqfF7AACAtilggpVhGJoyZYruuusuxcfHS5KqqqokSbGxsX61sbGx5lhVVZVCQ0MVFRV1yZqYmJgm7xkTE+NXc+H7REVFKTQ01Ky5mNzcXPO8LYfDobi4uCs5bAAAEEACJlhNnDhRH330kd54440mYzabze+5YRhNtl3owpqL1Ten5kIzZsyQz+czHxUVFZfsCwAABK6ACFaTJk3S22+/rQ0bNqhjx47mdqfTKUlNVoyqq6vN1SWn06m6ujp5vd5L1hw5cqTJ+x49etSv5sL38Xq9qq+vb7KS9WV2u12RkZF+DwAA0Da16mBlGIYmTpyo1atX6/3331fXrl39xrt27Sqn06mioiJzW11dnTZu3KgBAwZIkhISEhQSEuJXU1lZqbKyMrMmMTFRPp9P27ZtM2u2bt0qn8/nV1NWVqbKykqzprCwUHa7XQkJCdYfPAAACDit+pY2TzzxhF5//XX9z//8jyIiIswVI4fDobCwMNlsNmVlZSknJ0fdunVTt27dlJOTo3bt2ik9Pd2sHTdunLKzs9WhQwe1b99eU6dOVe/evTVs2DBJUo8ePTRixAhlZGRoyZIlkqTHHntMqamp6t69uyQpKSlJPXv2lNvt1rx583Ts2DFNnTpVGRkZrEIBAABJrTxYLV68WJI0ePBgv+3Lli3T2LFjJUlPPfWUTp8+rQkTJsjr9apfv34qLCxURESEWb9gwQIFBwdr1KhROn36tIYOHarly5crKCjIrFm5cqUyMzPNXw+mpaVp0aJF5nhQUJDWrl2rCRMmaODAgQoLC1N6errmz59/lY4eAAAEmoC6jlVbwHWsgLaN61gBbVObvI4VAABAa0awAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLtOorrwNAoAnEC/VyUVPAOqxYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhCuvA8A1jqvFA9ZhxQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACzCvQIBAAGH+xuitWLFCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIJ683wwsvvKB58+apsrJSvXr10sKFC3X33Xe3dFsAgFaME+6vDaxYXaE333xTWVlZevrpp7Vr1y7dfffd+tGPfqTy8vKWbg0AALQwgtUVysvL07hx4/Szn/1MPXr00MKFCxUXF6fFixe3dGsAAKCF8VXgFairq1NpaammT5/utz0pKUmbN2++6Gtqa2tVW1trPvf5fJKkmpoay/trrP3C8n0CAK5dnX7xp5Zu4YqVPZN8VfZ7/nPbMIxL1hGsrsA///lPNTQ0KDY21m97bGysqqqqLvqa3NxcPfPMM022x8XFXZUeAQC4ljkWXt39nzhxQg6H4yvHCVbNYLPZ/J4bhtFk23kzZszQlClTzOeNjY06duyYOnTo4PeampoaxcXFqaKiQpGRkVen8QDAPJzDPJzDPDAH5zEP5zAPLTcHhmHoxIkTcrlcl6wjWF2B6OhoBQUFNVmdqq6ubrKKdZ7dbpfdbvfbdsMNN3zle0RGRl6z/2f5MubhHObhHOaBOTiPeTiHeWiZObjUStV5nLx+BUJDQ5WQkKCioiK/7UVFRRowYEALdQUAAFoLVqyu0JQpU+R2u9W3b18lJibqpZdeUnl5uR5//PGWbg0AALQwgtUVeuCBB/Svf/1Ls2bNUmVlpeLj4/XOO++oc+fO32i/drtdv/nNb5p8bXitYR7OYR7OYR6Yg/OYh3OYh9Y/Bzbj6343CAAAgMvCOVYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWrcQLL7ygrl276vrrr1dCQoI++OCDlm7psuTm5ur73/++IiIiFBMTo3vvvVeffvqpX41hGJo5c6ZcLpfCwsI0ePBg7dmzx6+mtrZWkyZNUnR0tMLDw5WWlqaDBw/61Xi9XrndbjkcDjkcDrndbh0/ftyvpry8XCNHjlR4eLiio6OVmZmpurq6q3LsXyU3N1c2m01ZWVnmtmtlDg4dOqTRo0erQ4cOateunb73ve+ptLTUHL8W5uHs2bP65S9/qa5duyosLEy33HKLZs2apcbGRrOmLc7Dpk2bNHLkSLlcLtlsNr311lt+463tmHfv3q1BgwYpLCxMN998s2bNmvW194D7pvNQX1+vadOmqXfv3goPD5fL5dIjjzyiw4cPt6l5+Lp/C182fvx42Ww2LVy40G97QM+BgRaXn59vhISEGEuXLjU+/vhjY/LkyUZ4eLhx4MCBlm7tayUnJxvLli0zysrKDI/HY6SkpBidOnUyTp48adbMnj3biIiIMFatWmXs3r3beOCBB4ybbrrJqKmpMWsef/xx4+abbzaKioqMnTt3GkOGDDFuv/124+zZs2bNiBEjjPj4eGPz5s3G5s2bjfj4eCM1NdUcP3v2rBEfH28MGTLE2Llzp1FUVGS4XC5j4sSJ385kGIaxbds2o0uXLsZ3v/tdY/Lkyeb2a2EOjh07ZnTu3NkYO3assXXrVmP//v3GunXrjL/97W/X1Dz89re/NTp06GD85S9/Mfbv32/86U9/Mv7t3/7NWLhwYZueh3feecd4+umnjVWrVhmSjDVr1viNt6Zj9vl8RmxsrPHggw8au3fvNlatWmVEREQY8+fPv6rzcPz4cWPYsGHGm2++aXzyySfGli1bjH79+hkJCQl++wj0efi6fwvnrVmzxrj99tsNl8tlLFiwoM3MAcGqFbjzzjuNxx9/3G/bbbfdZkyfPr2FOmq+6upqQ5KxceNGwzAMo7Gx0XA6ncbs2bPNmjNnzhgOh8N48cUXDcM498cmJCTEyM/PN2sOHTpkXHfddUZBQYFhGIbx8ccfG5KMkpISs2bLli2GJOOTTz4xDOPc/5mvu+4649ChQ2bNG2+8YdjtdsPn8129g/5/Tpw4YXTr1s0oKioyBg0aZAara2UOpk2bZtx1111fOX6tzENKSorx6KOP+m27//77jdGjRxuGcW3Mw4Ufpq3tmF944QXD4XAYZ86cMWtyc3MNl8tlNDY2XrV5uJht27YZksz/kG5r8/BVc3Dw4EHj5ptvNsrKyozOnTv7BatAnwO+CmxhdXV1Ki0tVVJSkt/2pKQkbd68uYW6aj6fzydJat++vSRp//79qqqq8js+u92uQYMGmcdXWlqq+vp6vxqXy6X4+HizZsuWLXI4HOrXr59Z079/fzkcDr+a+Ph4vxtkJicnq7a21u/rqKvliSeeUEpKioYNG+a3/VqZg7ffflt9+/bVT37yE8XExKhPnz5aunSpOX6tzMNdd92l9evXa9++fZKkDz/8UMXFxbrnnnskXTvz8GWt7Zi3bNmiQYMG+V1gMjk5WYcPH9bnn39u/QRcgs/nk81mM+8hey3MQ2Njo9xut5588kn16tWryXigzwHBqoX985//VENDQ5ObOMfGxja52XNrZxiGpkyZorvuukvx8fGSZB7DpY6vqqpKoaGhioqKumRNTExMk/eMiYnxq7nwfaKiohQaGnrV5zI/P187d+5Ubm5uk7FrZQ7+8Y9/aPHixerWrZvee+89Pf7448rMzNSrr75q9ia1/XmYNm2aHnroId12220KCQlRnz59lJWVpYceesjsTWr78/Blre2YL1Zz/vm3OS9nzpzR9OnTlZ6ebt5M+FqYhzlz5ig4OFiZmZkXHQ/0OeCWNq2EzWbze24YRpNtrd3EiRP10Ucfqbi4uMlYc47vwpqL1TenxmoVFRWaPHmyCgsLdf31139lXVueA+ncf4X27dtXOTk5kqQ+ffpoz549Wrx4sR555JGv7K+tzcObb76pFStW6PXXX1evXr3k8XiUlZUll8ulMWPGfGV/bW0eLqY1HfPFevmq114N9fX1evDBB9XY2KgXXnjha+vbyjyUlpbqd7/7nXbu3HnF7xEoc8CKVQuLjo5WUFBQk2RcXV3dJEW3ZpMmTdLbb7+tDRs2qGPHjuZ2p9MpqWny//LxOZ1O1dXVyev1XrLmyJEjTd736NGjfjUXvo/X61V9ff1VncvS0lJVV1crISFBwcHBCg4O1saNG/X73/9ewcHBX/lfP21pDiTppptuUs+ePf229ejRQ+Xl5WZvUtufhyeffFLTp0/Xgw8+qN69e8vtdusXv/iFuZp5rczDl7W2Y75YTXV1taSmq2pXQ319vUaNGqX9+/erqKjIXK0631tbnocPPvhA1dXV6tSpk/n38sCBA8rOzlaXLl3MvgJ5DghWLSw0NFQJCQkqKiry215UVKQBAwa0UFeXzzAMTZw4UatXr9b777+vrl27+o137dpVTqfT7/jq6uq0ceNG8/gSEhIUEhLiV1NZWamysjKzJjExUT6fT9u2bTNrtm7dKp/P51dTVlamyspKs6awsFB2u10JCQnWH/z/M3ToUO3evVsej8d89O3bVw8//LA8Ho9uueWWNj8HkjRw4MAml9rYt2+feYPya+HfgiR98cUXuu46/z+tQUFB5uUWrpV5+LLWdsyJiYnatGmT38/uCwsL5XK5zA/3q+V8qPrss8+0bt06dejQwW+8rc+D2+3WRx995Pf30uVy6cknn9R7773XNuagWae8w1LnL7fw8ssvGx9//LGRlZVlhIeHG59//nlLt/a1fv7znxsOh8P4v//7P6OystJ8fPHFF2bN7NmzDYfDYaxevdrYvXu38dBDD130Z9YdO3Y01q1bZ+zcudP44Q9/eNGf1n73u981tmzZYmzZssXo3bv3RX9aO3ToUGPnzp3GunXrjI4dO36rl1s478u/CjSMa2MOtm3bZgQHBxvPPvus8dlnnxkrV6402rVrZ6xYseKamocxY8YYN998s3m5hdWrVxvR0dHGU0891abn4cSJE8auXbuMXbt2GZKMvLw8Y9euXeav3VrTMR8/ftyIjY01HnroIWP37t3G6tWrjcjISEsut3CpeaivrzfS0tKMjh07Gh6Px+9vZm1tbZuZh6/7t3ChC38VGOhzQLBqJf77v//b6Ny5sxEaGmrccccd5uUKWjtJF30sW7bMrGlsbDR+85vfGE6n07Db7cYPfvADY/fu3X77OX36tDFx4kSjffv2RlhYmJGammqUl5f71fzrX/8yHn74YSMiIsKIiIgwHn74YcPr9frVHDhwwEhJSTHCwsKM9u3bGxMnTvT7Ge235cJgda3Mwf/+7/8a8fHxht1uN2677TbjpZde8hu/FuahpqbGmDx5stGpUyfj+uuvN2655Rbj6aef9vvgbIvzsGHDhov+LRgzZkyrPOaPPvrIuPvuuw273W44nU5j5syZllxi4FLzsH///q/8m7lhw4Y2Mw9f92/hQhcLVoE8BzbDsOBSswAAAOAcKwAAAKsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCL/H/Wg8bJJ9gm1AAAAAElFTkSuQmCC\n", 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -644,11 +638,11 @@ " \n", " \n", " SalePrice\n", - " 66000\n", - " 57000\n", - " 10000\n", - " 38500\n", - " 11000\n", + " 66000.0\n", + " 57000.0\n", + " 10000.0\n", + " 38500.0\n", + " 11000.0\n", " \n", " \n", " MachineID\n", @@ -676,11 +670,11 @@ " \n", " \n", " auctioneerID\n", - " 3\n", - " 3\n", - " 3\n", - " 3\n", - " 3\n", + " 3.0\n", + " 3.0\n", + " 3.0\n", + " 3.0\n", + " 3.0\n", " \n", " \n", " YearMade\n", @@ -692,11 +686,11 @@ " \n", " \n", " MachineHoursCurrentMeter\n", - " 68\n", - " 4640\n", - " 2838\n", - " 3486\n", - " 722\n", + " 68.0\n", + " 4640.0\n", + " 2838.0\n", + " 3486.0\n", + " 722.0\n", " \n", " \n", " UsageBand\n", @@ -1065,13 +1059,13 @@ "text/plain": [ " 0 \\\n", "SalesID 1139246 \n", - "SalePrice 66000 \n", + "SalePrice 66000.0 \n", "MachineID 999089 \n", "ModelID 3157 \n", "datasource 121 \n", - "auctioneerID 3 \n", + "auctioneerID 3.0 \n", "YearMade 2004 \n", - "MachineHoursCurrentMeter 68 \n", + "MachineHoursCurrentMeter 68.0 \n", "UsageBand Low \n", "saledate 2006-11-16 00:00:00 \n", "fiModelDesc 521D \n", @@ -1120,13 +1114,13 @@ "\n", " 1 \\\n", "SalesID 1139248 \n", - "SalePrice 57000 \n", + "SalePrice 57000.0 \n", "MachineID 117657 \n", "ModelID 77 \n", "datasource 121 \n", - "auctioneerID 3 \n", + "auctioneerID 3.0 \n", "YearMade 1996 \n", - "MachineHoursCurrentMeter 4640 \n", + "MachineHoursCurrentMeter 4640.0 \n", "UsageBand Low \n", "saledate 2004-03-26 00:00:00 \n", "fiModelDesc 950FII \n", @@ -1175,13 +1169,13 @@ "\n", " 2 \\\n", "SalesID 1139249 \n", - "SalePrice 10000 \n", + "SalePrice 10000.0 \n", "MachineID 434808 \n", "ModelID 7009 \n", "datasource 121 \n", - "auctioneerID 3 \n", + "auctioneerID 3.0 \n", "YearMade 2001 \n", - "MachineHoursCurrentMeter 2838 \n", + "MachineHoursCurrentMeter 2838.0 \n", "UsageBand High \n", "saledate 2004-02-26 00:00:00 \n", "fiModelDesc 226 \n", @@ -1230,13 +1224,13 @@ "\n", " 3 \\\n", "SalesID 1139251 \n", - "SalePrice 38500 \n", + "SalePrice 38500.0 \n", "MachineID 1026470 \n", "ModelID 332 \n", "datasource 121 \n", - "auctioneerID 3 \n", + "auctioneerID 3.0 \n", "YearMade 2001 \n", - "MachineHoursCurrentMeter 3486 \n", + "MachineHoursCurrentMeter 3486.0 \n", "UsageBand High \n", "saledate 2011-05-19 00:00:00 \n", "fiModelDesc PC120-6E \n", @@ -1285,13 +1279,13 @@ "\n", " 4 \n", "SalesID 1139253 \n", - "SalePrice 11000 \n", + "SalePrice 11000.0 \n", "MachineID 1057373 \n", "ModelID 17311 \n", "datasource 121 \n", - "auctioneerID 3 \n", + "auctioneerID 3.0 \n", "YearMade 2007 \n", - "MachineHoursCurrentMeter 722 \n", + "MachineHoursCurrentMeter 722.0 \n", "UsageBand Medium \n", "saledate 2009-07-23 00:00:00 \n", "fiModelDesc S175 \n", @@ -1543,11 +1537,11 @@ " \n", " \n", " SalePrice\n", - " 9500\n", - " 14000\n", - " 50000\n", - " 16000\n", - " 22000\n", + " 9500.0\n", + " 14000.0\n", + " 50000.0\n", + " 16000.0\n", + " 22000.0\n", " \n", " \n", " MachineID\n", @@ -1575,11 +1569,11 @@ " \n", " \n", " auctioneerID\n", - " 18\n", - " 99\n", - " 99\n", - " 99\n", - " 99\n", + " 18.0\n", + " 99.0\n", + " 99.0\n", + " 99.0\n", + " 99.0\n", " \n", " \n", " YearMade\n", @@ -1996,11 +1990,11 @@ "text/plain": [ " 205615 \\\n", "SalesID 1646770 \n", - "SalePrice 9500 \n", + "SalePrice 9500.0 \n", "MachineID 1126363 \n", "ModelID 8434 \n", "datasource 132 \n", - "auctioneerID 18 \n", + "auctioneerID 18.0 \n", "YearMade 1974 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -2055,11 +2049,11 @@ "\n", " 274835 \\\n", "SalesID 1821514 \n", - "SalePrice 14000 \n", + "SalePrice 14000.0 \n", "MachineID 1194089 \n", "ModelID 10150 \n", "datasource 132 \n", - "auctioneerID 99 \n", + "auctioneerID 99.0 \n", "YearMade 1980 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -2114,11 +2108,11 @@ "\n", " 141296 \\\n", "SalesID 1505138 \n", - "SalePrice 50000 \n", + "SalePrice 50000.0 \n", "MachineID 1473654 \n", "ModelID 4139 \n", "datasource 132 \n", - "auctioneerID 99 \n", + "auctioneerID 99.0 \n", "YearMade 1978 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -2173,11 +2167,11 @@ "\n", " 212552 \\\n", "SalesID 1671174 \n", - "SalePrice 16000 \n", + "SalePrice 16000.0 \n", "MachineID 1327630 \n", "ModelID 8591 \n", "datasource 132 \n", - "auctioneerID 99 \n", + "auctioneerID 99.0 \n", "YearMade 1980 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -2232,11 +2226,11 @@ "\n", " 62755 \n", "SalesID 1329056 \n", - "SalePrice 22000 \n", + "SalePrice 22000.0 \n", "MachineID 1336053 \n", "ModelID 4089 \n", "datasource 132 \n", - "auctioneerID 99 \n", + "auctioneerID 99.0 \n", "YearMade 1984 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -2393,20 +2387,25 @@ { "cell_type": "code", "execution_count": 17, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "ename": "ValueError", "evalue": "could not convert string to float: 'Low'", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_tmp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SalePrice\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m 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ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m 529\u001b[0m \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m 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347\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 348\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sample_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 349\u001b[0m sample_weight \u001b[38;5;241m=\u001b[39m _check_sample_weight(sample_weight, X)\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:565\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m 563\u001b[0m y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[0;32m 564\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 565\u001b[0m X, y \u001b[38;5;241m=\u001b[39m check_X_y(X, y, 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\u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 1103\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires y to be passed, but the target y is None\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1104\u001b[0m )\n\u001b[1;32m-> 1106\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1107\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1108\u001b[0m \u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1109\u001b[0m \u001b[43m \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_large_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1110\u001b[0m \u001b[43m 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\u001b[49m\u001b[43mensure_min_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1117\u001b[0m \u001b[43m \u001b[49m\u001b[43mensure_min_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1118\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1119\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1120\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1122\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric, estimator\u001b[38;5;241m=\u001b[39mestimator)\n\u001b[0;32m 1124\u001b[0m check_consistent_length(X, y)\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:879\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m 877\u001b[0m array \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(array, dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m 878\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 879\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[43m_asarray_with_order\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 880\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[0;32m 881\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 882\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[0;32m 883\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\_array_api.py:185\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[1;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[0;32m 182\u001b[0m xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(array)\n\u001b[0;32m 183\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m xp\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy.array_api\u001b[39m\u001b[38;5;124m\"\u001b[39m}:\n\u001b[0;32m 184\u001b[0m \u001b[38;5;66;03m# Use NumPy API to support order\u001b[39;00m\n\u001b[1;32m--> 185\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[43mnumpy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 186\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39masarray(array, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m 187\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:2070\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m 2069\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype: npt\u001b[38;5;241m.\u001b[39mDTypeLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m-> 2070\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Low'" ] } ], @@ -2632,11 +2631,11 @@ " \n", " \n", " SalePrice\n", - " 9500\n", - " 14000\n", - " 50000\n", - " 16000\n", - " 22000\n", + " 9500.0\n", + " 14000.0\n", + " 50000.0\n", + " 16000.0\n", + " 22000.0\n", " \n", " \n", " MachineID\n", @@ -2664,11 +2663,11 @@ " \n", " \n", " auctioneerID\n", - " 18\n", - " 99\n", - " 99\n", - " 99\n", - " 99\n", + " 18.0\n", + " 99.0\n", + " 99.0\n", + " 99.0\n", + " 99.0\n", " \n", " \n", " YearMade\n", @@ -3085,11 +3084,11 @@ "text/plain": [ " 205615 \\\n", "SalesID 1646770 \n", - "SalePrice 9500 \n", + "SalePrice 9500.0 \n", "MachineID 1126363 \n", "ModelID 8434 \n", "datasource 132 \n", - "auctioneerID 18 \n", + "auctioneerID 18.0 \n", "YearMade 1974 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -3144,11 +3143,11 @@ "\n", " 274835 \\\n", "SalesID 1821514 \n", - "SalePrice 14000 \n", + "SalePrice 14000.0 \n", "MachineID 1194089 \n", "ModelID 10150 \n", "datasource 132 \n", - "auctioneerID 99 \n", + "auctioneerID 99.0 \n", "YearMade 1980 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -3203,11 +3202,11 @@ "\n", " 141296 \\\n", "SalesID 1505138 \n", - "SalePrice 50000 \n", + "SalePrice 50000.0 \n", "MachineID 1473654 \n", "ModelID 4139 \n", "datasource 132 \n", - "auctioneerID 99 \n", + "auctioneerID 99.0 \n", "YearMade 1978 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -3262,11 +3261,11 @@ "\n", " 212552 \\\n", "SalesID 1671174 \n", - "SalePrice 16000 \n", + "SalePrice 16000.0 \n", "MachineID 1327630 \n", "ModelID 8591 \n", "datasource 132 \n", - "auctioneerID 99 \n", + "auctioneerID 99.0 \n", "YearMade 1980 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -3321,11 +3320,11 @@ "\n", " 62755 \n", "SalesID 1329056 \n", - "SalePrice 22000 \n", + "SalePrice 22000.0 \n", "MachineID 1336053 \n", "ModelID 4089 \n", "datasource 132 \n", - "auctioneerID 99 \n", + "auctioneerID 99.0 \n", "YearMade 1984 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -3581,7 +3580,7 @@ " 55 saleDayofweek 412698 non-null int64 \n", " 56 saleDayofyear 412698 non-null int64 \n", "dtypes: category(44), float64(3), int64(10)\n", - "memory usage: 63.3 MB\n" + "memory usage: 63.2 MB\n" ] } ], @@ -3799,11 +3798,11 @@ " \n", " \n", " SalePrice\n", - " 9500\n", - " 14000\n", - " 50000\n", - " 16000\n", - " 22000\n", + " 9500.0\n", + " 14000.0\n", + " 50000.0\n", + " 16000.0\n", + " 22000.0\n", " \n", " \n", " MachineID\n", @@ -3831,11 +3830,11 @@ " \n", " \n", " auctioneerID\n", - " 18\n", - " 99\n", - " 99\n", - " 99\n", - " 99\n", + " 18.0\n", + " 99.0\n", + " 99.0\n", + " 99.0\n", + " 99.0\n", " \n", " \n", " YearMade\n", @@ -4252,11 +4251,11 @@ "text/plain": [ " 0 \\\n", "SalesID 1646770 \n", - "SalePrice 9500 \n", + "SalePrice 9500.0 \n", "MachineID 1126363 \n", "ModelID 8434 \n", "datasource 132 \n", - "auctioneerID 18 \n", + "auctioneerID 18.0 \n", "YearMade 1974 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -4311,11 +4310,11 @@ "\n", " 1 \\\n", "SalesID 1821514 \n", - "SalePrice 14000 \n", + "SalePrice 14000.0 \n", "MachineID 1194089 \n", "ModelID 10150 \n", "datasource 132 \n", - "auctioneerID 99 \n", + "auctioneerID 99.0 \n", "YearMade 1980 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -4370,11 +4369,11 @@ "\n", " 2 \\\n", "SalesID 1505138 \n", - "SalePrice 50000 \n", + "SalePrice 50000.0 \n", "MachineID 1473654 \n", "ModelID 4139 \n", "datasource 132 \n", - "auctioneerID 99 \n", + "auctioneerID 99.0 \n", "YearMade 1978 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -4429,11 +4428,11 @@ "\n", " 3 \\\n", "SalesID 1671174 \n", - "SalePrice 16000 \n", + "SalePrice 16000.0 \n", "MachineID 1327630 \n", "ModelID 8591 \n", "datasource 132 \n", - "auctioneerID 99 \n", + "auctioneerID 99.0 \n", "YearMade 1980 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -4488,11 +4487,11 @@ "\n", " 4 \n", "SalesID 1329056 \n", - "SalePrice 22000 \n", + "SalePrice 22000.0 \n", "MachineID 1336053 \n", "ModelID 4089 \n", "datasource 132 \n", - "auctioneerID 99 \n", + "auctioneerID 99.0 \n", "YearMade 1984 \n", "MachineHoursCurrentMeter NaN \n", "UsageBand NaN \n", @@ -4960,11 +4959,11 @@ " \n", " \n", " SalePrice\n", - " 9500\n", - " 14000\n", - " 50000\n", - " 16000\n", - " 22000\n", + " 9500.0\n", + " 14000.0\n", + " 50000.0\n", + " 16000.0\n", + " 22000.0\n", " \n", " \n", " MachineID\n", @@ -5046,7 +5045,7 @@ "text/plain": [ " 0 1 2 3 4\n", "SalesID 1646770 1821514 1505138 1671174 1329056\n", - "SalePrice 9500 14000 50000 16000 22000\n", + "SalePrice 9500.0 14000.0 50000.0 16000.0 22000.0\n", "MachineID 1126363 1194089 1473654 1327630 1336053\n", "ModelID 8434 10150 4139 8591 4089\n", "datasource 132 132 132 132 132\n", @@ -5089,20 +5088,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 25min 34s, sys: 10.4 s, total: 25min 44s\n", - "Wall time: 1min 49s\n" + "CPU times: total: 42min 28s\n", + "Wall time: 11min 24s\n" ] }, { "data": { + "text/html": [ + "
RandomForestRegressor(n_jobs=-1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ - "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=-1, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)" + "RandomForestRegressor(n_jobs=-1)" ] }, "execution_count": 42, @@ -5127,7 +5123,7 @@ { "data": { "text/plain": [ - "0.987621368497284" + "0.9876162814378249" ] }, "execution_count": 43, @@ -5526,7 +5522,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -5564,7 +5560,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -5578,7 +5574,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -5587,7 +5583,7 @@ "401125" ] }, - "execution_count": 51, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -5607,7 +5603,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -5627,30 +5623,27 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 49.2 s, sys: 897 ms, total: 50.1 s\n", - "Wall time: 5.07 s\n" + "CPU times: total: 1min 29s\n", + "Wall time: 23.9 s\n" ] }, { "data": { + "text/html": [ + "
RandomForestRegressor(max_samples=10000, n_jobs=-1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ - "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=10000, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=-1, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)" + "RandomForestRegressor(max_samples=10000, n_jobs=-1)" ] }, - "execution_count": 53, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -5663,21 +5656,21 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'Training MAE': 5570.442946637581,\n", - " 'Valid MAE': 7152.537319623261,\n", - " 'Training RMSLE': 0.2579466072796448,\n", - " 'Valid RMSLE': 0.2934498553410761,\n", - " 'Training R^2': 0.8601748180348622,\n", - " 'Valid R^2': 0.8325057428307028}" + "{'Training MAE': 5556.750853574322,\n", + " 'Valid MAE': 7164.1214136351855,\n", + " 'Training RMSLE': 0.2574197906921008,\n", + " 'Valid RMSLE': 0.2926166424421471,\n", + " 'Training R^2': 0.8607024803273955,\n", + " 'Valid R^2': 0.8326755284326167}" ] }, - "execution_count": 54, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -5708,62 +5701,52 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", - "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed: 6.4min finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 6min 28s, sys: 7.53 s, total: 6min 36s\n", - "Wall time: 6min 36s\n" + "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n", + "CPU times: total: 4min 50s\n", + "Wall time: 4min 51s\n" ] }, { "data": { + "text/html": [ + "
RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=20,\n",
+       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
+       "                                        'max_features': [0.5, 1, 'sqrt',\n",
+       "                                                         'log2'],\n",
+       "                                        'max_samples': [10000],\n",
+       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
+       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
+       "                                        'n_estimators': array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
+       "                   verbose=True)
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" + ], "text/plain": [ - "RandomizedSearchCV(cv=5, error_score=nan,\n", - " estimator=RandomForestRegressor(bootstrap=True,\n", - " ccp_alpha=0.0,\n", - " criterion='mse',\n", - " max_depth=None,\n", - " max_features='auto',\n", - " max_leaf_nodes=None,\n", - " max_samples=None,\n", - " min_impurity_decrease=0.0,\n", - " min_impurity_split=None,\n", - " min_samples_leaf=1,\n", - " min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0,\n", - " n_estimators=100,\n", - " n_jobs=None, oob_score=Fals...\n", + "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=20,\n", " param_distributions={'max_depth': [None, 3, 5, 10],\n", " 'max_features': [0.5, 1, 'sqrt',\n", - " 'auto'],\n", + " 'log2'],\n", " 'max_samples': [10000],\n", " 'min_samples_leaf': array([ 1, 3, 5, 7, 9, 11, 13, 15, 17, 19]),\n", " 'min_samples_split': array([ 2, 4, 6, 8, 10, 12, 14, 16, 18]),\n", " 'n_estimators': array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n", - " pre_dispatch='2*n_jobs', random_state=None, refit=True,\n", - " return_train_score=False, scoring=None, verbose=True)" + " verbose=True)" ] }, - "execution_count": 57, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -5777,7 +5760,7 @@ " \"max_depth\": [None, 3, 5, 10],\n", " \"min_samples_split\": np.arange(2, 20, 2),\n", " \"min_samples_leaf\": np.arange(1, 20, 2),\n", - " \"max_features\": [0.5, 1, \"sqrt\", \"auto\"],\n", + " \"max_features\": [0.5, 1, \"sqrt\", \"log2\"], # max_features = 'auto' removed in v 1.3\n", " \"max_samples\": [10000]}\n", "\n", "rs_model = RandomizedSearchCV(RandomForestRegressor(),\n", @@ -5791,21 +5774,21 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'n_estimators': 90,\n", + "{'n_estimators': 30,\n", " 'min_samples_split': 4,\n", - " 'min_samples_leaf': 1,\n", + " 'min_samples_leaf': 13,\n", " 'max_samples': 10000,\n", " 'max_features': 0.5,\n", " 'max_depth': None}" ] }, - "execution_count": 58, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -5817,21 +5800,21 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'Training MAE': 5739.3439073046875,\n", - " 'Valid MAE': 7192.1275124731565,\n", - " 'Training RMSLE': 0.2645974926906147,\n", - " 'Valid RMSLE': 0.29647910600319566,\n", - " 'Training R^2': 0.8542568601075173,\n", - " 'Valid R^2': 0.8366783805921302}" + "{'Training MAE': 6657.10150634419,\n", + " 'Valid MAE': 7941.865289133009,\n", + " 'Training RMSLE': 0.29866126182791447,\n", + " 'Valid RMSLE': 0.32164678319767914,\n", + " 'Training R^2': 0.8052999128283345,\n", + " 'Valid R^2': 0.7965864883043403}" ] }, - "execution_count": 59, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -5856,30 +5839,30 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 11min 45s, sys: 5.53 s, total: 11min 50s\n", - "Wall time: 54 s\n" + "CPU times: total: 15min 40s\n", + "Wall time: 4min 3s\n" ] }, { "data": { + "text/html": [ + "
RandomForestRegressor(max_features=0.5, min_samples_split=14, n_estimators=90,\n",
+       "                      n_jobs=-1)
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" + ], "text/plain": [ - "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features=0.5, max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=14, min_weight_fraction_leaf=0.0,\n", - " n_estimators=90, n_jobs=-1, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)" + "RandomForestRegressor(max_features=0.5, min_samples_split=14, n_estimators=90,\n", + " n_jobs=-1)" ] }, - "execution_count": 60, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -5898,21 +5881,21 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'Training MAE': 2927.555314630477,\n", - " 'Valid MAE': 5925.139309561446,\n", - " 'Training RMSLE': 0.1433128908407748,\n", - " 'Valid RMSLE': 0.2453295099556409,\n", - " 'Training R^2': 0.9596919518640112,\n", - " 'Valid R^2': 0.883030690228852}" + "{'Training MAE': 2929.2328822701343,\n", + " 'Valid MAE': 5887.99362921498,\n", + " 'Training RMSLE': 0.14342607745574906,\n", + " 'Valid RMSLE': 0.2431764149190233,\n", + " 'Training R^2': 0.9596584818950937,\n", + " 'Valid R^2': 0.8843095309214519}" ] }, - "execution_count": 61, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -5934,30 +5917,30 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 4min 59s, sys: 2.76 s, total: 5min 2s\n", - "Wall time: 25.3 s\n" + "CPU times: total: 7min 5s\n", + "Wall time: 1min 54s\n" ] }, { "data": { + "text/html": [ + "
RandomForestRegressor(max_features=0.5, min_samples_leaf=3, n_estimators=40,\n",
+       "                      n_jobs=-1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ - "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features=0.5, max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=3,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=40, n_jobs=-1, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)" + "RandomForestRegressor(max_features=0.5, min_samples_leaf=3, n_estimators=40,\n", + " n_jobs=-1)" ] }, - "execution_count": 62, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -5974,21 +5957,21 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'Training MAE': 2538.086916839285,\n", - " 'Valid MAE': 5921.566785123334,\n", - " 'Training RMSLE': 0.12938511430324895,\n", - " 'Valid RMSLE': 0.24299588986662657,\n", - " 'Training R^2': 0.9672433268997953,\n", - " 'Valid R^2': 0.8812702169749176}" + "{'Training MAE': 2545.7093438579336,\n", + " 'Valid MAE': 5936.518532437765,\n", + " 'Training RMSLE': 0.12968835433080783,\n", + " 'Valid RMSLE': 0.24459085064058816,\n", + " 'Training R^2': 0.9670744090598806,\n", + " 'Valid R^2': 0.8807459238442099}" ] }, - "execution_count": 63, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -6014,7 +5997,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -6226,7 +6209,7 @@ "[5 rows x 52 columns]" ] }, - "execution_count": 64, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -6239,23 +6222,22 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 62, "metadata": {}, "outputs": [ { "ename": "ValueError", - "evalue": "could not convert string to float: 'Low'", + "evalue": "The feature names should match those that were passed during fit.\nFeature names unseen at fit time:\n- saledate\nFeature names seen at fit time, yet now missing:\n- Backhoe_Mounting_is_missing\n- Blade_Extension_is_missing\n- Blade_Type_is_missing\n- Blade_Width_is_missing\n- Coupler_System_is_missing\n- ...\n", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Let's see how the model goes predicting on the test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.7/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 764\u001b[0m \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 766\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 768\u001b[0m \u001b[0;31m# Assign chunk of trees to jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.7/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.7/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X, check_input)\u001b[0m\n\u001b[1;32m 378\u001b[0m \u001b[0;34m\"\"\"Validate X whenever one tries to predict, apply, predict_proba\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 380\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 381\u001b[0m if issparse(X) and (X.indices.dtype != np.intc or\n\u001b[1;32m 382\u001b[0m X.indptr.dtype != np.intc):\n", - "\u001b[0;32m~/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m 529\u001b[0m \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 532\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 533\u001b[0m raise ValueError(\"Complex data not supported\\n\"\n", - "\u001b[0;32m~/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.7/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Low'" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[62], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Let's see how the model goes predicting on the test data\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_test\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:981\u001b[0m, in \u001b[0;36mForestRegressor.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 979\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m 980\u001b[0m \u001b[38;5;66;03m# Check data\u001b[39;00m\n\u001b[1;32m--> 981\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_X_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 983\u001b[0m \u001b[38;5;66;03m# Assign chunk of trees to jobs\u001b[39;00m\n\u001b[0;32m 984\u001b[0m n_jobs, _, _ \u001b[38;5;241m=\u001b[39m _partition_estimators(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:602\u001b[0m, in \u001b[0;36mBaseForest._validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 599\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 600\u001b[0m \u001b[38;5;124;03mValidate X whenever one tries to predict, apply, predict_proba.\"\"\"\u001b[39;00m\n\u001b[0;32m 601\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m--> 602\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 603\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X) \u001b[38;5;129;01mand\u001b[39;00m (X\u001b[38;5;241m.\u001b[39mindices\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc \u001b[38;5;129;01mor\u001b[39;00m X\u001b[38;5;241m.\u001b[39mindptr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc):\n\u001b[0;32m 604\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo support for np.int64 index based sparse matrices\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:529\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m 464\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_data\u001b[39m(\n\u001b[0;32m 465\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 466\u001b[0m X\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno_validation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 470\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params,\n\u001b[0;32m 471\u001b[0m ):\n\u001b[0;32m 472\u001b[0m \u001b[38;5;124;03m\"\"\"Validate input data and set or check the `n_features_in_` attribute.\u001b[39;00m\n\u001b[0;32m 473\u001b[0m \n\u001b[0;32m 474\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 527\u001b[0m \u001b[38;5;124;03m validated.\u001b[39;00m\n\u001b[0;32m 528\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 529\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_feature_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 531\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_tags()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires_y\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 532\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 533\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m estimator \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 534\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires y to be passed, but the target y is None.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 535\u001b[0m )\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:462\u001b[0m, in \u001b[0;36mBaseEstimator._check_feature_names\u001b[1;34m(self, X, reset)\u001b[0m\n\u001b[0;32m 457\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m missing_names \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m unexpected_names:\n\u001b[0;32m 458\u001b[0m message \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 459\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature names must be in the same order as they were in fit.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 460\u001b[0m )\n\u001b[1;32m--> 462\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(message)\n", + "\u001b[1;31mValueError\u001b[0m: The feature names should match those that were passed during fit.\nFeature names unseen at fit time:\n- saledate\nFeature names seen at fit time, yet now missing:\n- Backhoe_Mounting_is_missing\n- Blade_Extension_is_missing\n- Blade_Type_is_missing\n- Blade_Width_is_missing\n- Coupler_System_is_missing\n- ...\n" ] } ], @@ -6288,7 +6270,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -6332,7 +6314,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -6558,7 +6540,7 @@ "[5 rows x 101 columns]" ] }, - "execution_count": 67, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -6570,7 +6552,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -6796,7 +6778,7 @@ "[5 rows x 102 columns]" ] }, - "execution_count": 68, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -6807,21 +6789,22 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 66, "metadata": {}, "outputs": [ { "ename": "ValueError", - "evalue": "Number of features of the model must match the input. Model n_features is 102 and input n_features is 101 ", + "evalue": "The feature names should match those that were passed during fit.\nFeature names seen at fit time, yet now missing:\n- auctioneerID_is_missing\n", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Make predictions on the test dataset using the best model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtest_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mideal_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.7/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 764\u001b[0m \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 766\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 768\u001b[0m \u001b[0;31m# Assign chunk of trees to jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.7/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/ml-course/zero-to-mastery-ml/env/lib/python3.7/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X, check_input)\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[0;34m\"match the input. Model n_features is %s and \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0;34m\"input n_features is %s \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m % (self.n_features_, n_features))\n\u001b[0m\u001b[1;32m 392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 393\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Number of features of the model must match the input. Model n_features is 102 and input n_features is 101 " + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[66], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Make predictions on the test dataset using the best model\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m test_preds \u001b[38;5;241m=\u001b[39m \u001b[43mideal_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_test\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:981\u001b[0m, in \u001b[0;36mForestRegressor.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 979\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m 980\u001b[0m \u001b[38;5;66;03m# Check data\u001b[39;00m\n\u001b[1;32m--> 981\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_X_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 983\u001b[0m \u001b[38;5;66;03m# Assign chunk of trees to jobs\u001b[39;00m\n\u001b[0;32m 984\u001b[0m n_jobs, _, _ \u001b[38;5;241m=\u001b[39m _partition_estimators(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:602\u001b[0m, in \u001b[0;36mBaseForest._validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 599\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 600\u001b[0m \u001b[38;5;124;03mValidate X whenever one tries to predict, apply, predict_proba.\"\"\"\u001b[39;00m\n\u001b[0;32m 601\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m--> 602\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 603\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X) \u001b[38;5;129;01mand\u001b[39;00m (X\u001b[38;5;241m.\u001b[39mindices\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc \u001b[38;5;129;01mor\u001b[39;00m X\u001b[38;5;241m.\u001b[39mindptr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc):\n\u001b[0;32m 604\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo support for np.int64 index based sparse matrices\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:529\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m 464\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_data\u001b[39m(\n\u001b[0;32m 465\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 466\u001b[0m X\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno_validation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 470\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params,\n\u001b[0;32m 471\u001b[0m ):\n\u001b[0;32m 472\u001b[0m \u001b[38;5;124;03m\"\"\"Validate input data and set or check the `n_features_in_` attribute.\u001b[39;00m\n\u001b[0;32m 473\u001b[0m \n\u001b[0;32m 474\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 527\u001b[0m \u001b[38;5;124;03m validated.\u001b[39;00m\n\u001b[0;32m 528\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 529\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_feature_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 531\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_tags()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires_y\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 532\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 533\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m estimator \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 534\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires y to be passed, but the target y is None.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 535\u001b[0m )\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:462\u001b[0m, in \u001b[0;36mBaseEstimator._check_feature_names\u001b[1;34m(self, X, reset)\u001b[0m\n\u001b[0;32m 457\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m missing_names \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m unexpected_names:\n\u001b[0;32m 458\u001b[0m message \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 459\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature names must be in the same order as they were in fit.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 460\u001b[0m )\n\u001b[1;32m--> 462\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(message)\n", + "\u001b[1;31mValueError\u001b[0m: The feature names should match those that were passed during fit.\nFeature names seen at fit time, yet now missing:\n- auctioneerID_is_missing\n" ] } ], @@ -6841,7 +6824,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -6850,7 +6833,7 @@ "{'auctioneerID_is_missing'}" ] }, - "execution_count": 70, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -6871,7 +6854,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -7097,7 +7080,7 @@ "[5 rows x 102 columns]" ] }, - "execution_count": 71, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -7115,10 +7098,314 @@ "Now the test dataset matches the training dataset, we should be able to make predictions on it using our trained model. " ] }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The feature names should match those that were passed during fit.\nFeature names must be in the same order as they were in fit.\n", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[69], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Make predictions on the test dataset using the best model\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m test_preds \u001b[38;5;241m=\u001b[39m \u001b[43mideal_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_test\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:981\u001b[0m, in \u001b[0;36mForestRegressor.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 979\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m 980\u001b[0m \u001b[38;5;66;03m# Check data\u001b[39;00m\n\u001b[1;32m--> 981\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_X_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 983\u001b[0m \u001b[38;5;66;03m# Assign chunk of trees to jobs\u001b[39;00m\n\u001b[0;32m 984\u001b[0m n_jobs, _, _ \u001b[38;5;241m=\u001b[39m _partition_estimators(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:602\u001b[0m, in \u001b[0;36mBaseForest._validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 599\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 600\u001b[0m \u001b[38;5;124;03mValidate X whenever one tries to predict, apply, predict_proba.\"\"\"\u001b[39;00m\n\u001b[0;32m 601\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m--> 602\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 603\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X) \u001b[38;5;129;01mand\u001b[39;00m (X\u001b[38;5;241m.\u001b[39mindices\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc \u001b[38;5;129;01mor\u001b[39;00m X\u001b[38;5;241m.\u001b[39mindptr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc):\n\u001b[0;32m 604\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo support for np.int64 index based sparse matrices\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:529\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m 464\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_data\u001b[39m(\n\u001b[0;32m 465\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 466\u001b[0m X\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno_validation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 470\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params,\n\u001b[0;32m 471\u001b[0m ):\n\u001b[0;32m 472\u001b[0m \u001b[38;5;124;03m\"\"\"Validate input data and set or check the `n_features_in_` attribute.\u001b[39;00m\n\u001b[0;32m 473\u001b[0m \n\u001b[0;32m 474\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 527\u001b[0m \u001b[38;5;124;03m validated.\u001b[39;00m\n\u001b[0;32m 528\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 529\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_feature_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 531\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_tags()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires_y\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 532\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 533\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m estimator \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 534\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires y to be passed, but the target y is None.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 535\u001b[0m )\n", + "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:462\u001b[0m, in \u001b[0;36mBaseEstimator._check_feature_names\u001b[1;34m(self, X, reset)\u001b[0m\n\u001b[0;32m 457\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m missing_names \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m unexpected_names:\n\u001b[0;32m 458\u001b[0m message \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 459\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature names must be in the same order as they were in fit.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 460\u001b[0m )\n\u001b[1;32m--> 462\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(message)\n", + "\u001b[1;31mValueError\u001b[0m: The feature names should match those that were passed during fit.\nFeature names must be in the same order as they were in fit.\n" + ] + } + ], + "source": [ + "# Make predictions on the test dataset using the best model\n", + "test_preds = ideal_model.predict(df_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This error throws because `auctioneerID_is_missing` column is in another position in X_train. Let's fix the problem manually." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "56" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's see what is the position of 'auctioneerID_is_missing' in X_train\n", + "X_train.columns.get_loc('auctioneerID_is_missing')" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "# remove the existing column and Set the column at 56 position\n", + "df_test.drop('auctioneerID_is_missing', axis=1, inplace=True)" + ] + }, { "cell_type": "code", "execution_count": 72, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SalesIDMachineIDModelIDdatasourceauctioneerIDYearMadeMachineHoursCurrentMeterUsageBandfiModelDescfiBaseModel...Undercarriage_Pad_Width_is_missingStick_Length_is_missingThumb_is_missingPattern_Changer_is_missingGrouser_Type_is_missingBackhoe_Mounting_is_missingBlade_Type_is_missingTravel_Controls_is_missingDifferential_Type_is_missingSteering_Controls_is_missing
0122782910063093168121319993688.02499180...TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue
11227844102281772711213100028555.01831292...TrueTrueTrueTrueTrueTrueTrueTrueFalseFalse
21227847103156022805121320046038.031177404...FalseFalseFalseFalseFalseTrueTrueTrueTrueTrue
31227848562041269121320068940.01287113...FalseFalseFalseFalseFalseTrueTrueTrueTrueTrue
41227863105388722312121320052286.02566196...TrueTrueTrueTrueTrueFalseFalseFalseTrueTrue
\n", + "

5 rows × 102 columns

\n", + "
" + ], + "text/plain": [ + " SalesID MachineID ModelID datasource auctioneerID YearMade \\\n", + "0 1227829 1006309 3168 121 3 1999 \n", + "1 1227844 1022817 7271 121 3 1000 \n", + "2 1227847 1031560 22805 121 3 2004 \n", + "3 1227848 56204 1269 121 3 2006 \n", + "4 1227863 1053887 22312 121 3 2005 \n", + "\n", + " MachineHoursCurrentMeter UsageBand fiModelDesc fiBaseModel ... \\\n", + "0 3688.0 2 499 180 ... \n", + "1 28555.0 1 831 292 ... \n", + "2 6038.0 3 1177 404 ... \n", + "3 8940.0 1 287 113 ... \n", + "4 2286.0 2 566 196 ... \n", + "\n", + " Undercarriage_Pad_Width_is_missing Stick_Length_is_missing \\\n", + "0 True True \n", + "1 True True \n", + "2 False False \n", + "3 False False \n", + "4 True True \n", + "\n", + " Thumb_is_missing Pattern_Changer_is_missing Grouser_Type_is_missing \\\n", + "0 True True True \n", + "1 True True True \n", + "2 False False False \n", + "3 False False False \n", + "4 True True True \n", + "\n", + " Backhoe_Mounting_is_missing Blade_Type_is_missing \\\n", + "0 True True \n", + "1 True True \n", + "2 True True \n", + "3 True True \n", + "4 False False \n", + "\n", + " Travel_Controls_is_missing Differential_Type_is_missing \\\n", + "0 True True \n", + "1 True False \n", + "2 True True \n", + "3 True True \n", + "4 False True \n", + "\n", + " Steering_Controls_is_missing \n", + "0 True \n", + "1 False \n", + "2 True \n", + "3 True \n", + "4 True \n", + "\n", + "[5 rows x 102 columns]" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_test.insert(loc=56,\n", + " column='auctioneerID_is_missing',\n", + " value=False)\n", + "df_test.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, "outputs": [], "source": [ "# Make predictions on the test dataset using the best model\n", @@ -7136,7 +7423,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -7168,27 +7455,27 @@ " \n", " 0\n", " 1227829\n", - " 20256.936017\n", + " 17136.260306\n", " \n", " \n", " 1\n", " 1227844\n", - " 18324.603777\n", + " 16136.249482\n", " \n", " \n", " 2\n", " 1227847\n", - " 49532.610965\n", + " 47196.056308\n", " \n", " \n", " 3\n", " 1227848\n", - " 60273.620071\n", + " 64502.748818\n", " \n", " \n", " 4\n", " 1227863\n", - " 48296.569897\n", + " 54870.431933\n", " \n", " \n", " ...\n", @@ -7198,27 +7485,27 @@ " \n", " 12452\n", " 6643171\n", - " 43937.338316\n", + " 42177.624902\n", " \n", " \n", " 12453\n", " 6643173\n", - " 16042.221329\n", + " 13546.067550\n", " \n", " \n", " 12454\n", " 6643184\n", - " 16800.163465\n", + " 13722.151136\n", " \n", " \n", " 12455\n", " 6643186\n", - " 21127.577610\n", + " 18227.549049\n", " \n", " \n", " 12456\n", " 6643196\n", - " 30649.491898\n", + " 27728.474296\n", " \n", " \n", "\n", @@ -7227,22 +7514,22 @@ ], "text/plain": [ " SalesID SalePrice\n", - "0 1227829 20256.936017\n", - "1 1227844 18324.603777\n", - "2 1227847 49532.610965\n", - "3 1227848 60273.620071\n", - "4 1227863 48296.569897\n", + "0 1227829 17136.260306\n", + "1 1227844 16136.249482\n", + "2 1227847 47196.056308\n", + "3 1227848 64502.748818\n", + "4 1227863 54870.431933\n", "... ... ...\n", - "12452 6643171 43937.338316\n", - "12453 6643173 16042.221329\n", - "12454 6643184 16800.163465\n", - "12455 6643186 21127.577610\n", - "12456 6643196 30649.491898\n", + "12452 6643171 42177.624902\n", + "12453 6643173 13546.067550\n", + "12454 6643184 13722.151136\n", + "12455 6643186 18227.549049\n", + "12456 6643196 27728.474296\n", "\n", "[12457 rows x 2 columns]" ] }, - "execution_count": 73, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -7257,7 +7544,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -7289,41 +7576,41 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 76, "metadata": {}, "outputs": [ 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+7633,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -7367,19 +7654,17 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 79, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -7389,16 +7674,16 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1.0" + "0.9999999999999997" ] }, - "execution_count": 82, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -7409,7 +7694,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -7418,7 +7703,7 @@ "216605" ] }, - "execution_count": 83, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -7429,7 +7714,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -7444,7 +7729,7 @@ "Name: ProductSize, dtype: int64" ] }, - "execution_count": 84, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -7455,7 +7740,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -7466,7 +7751,7 @@ "Name: Turbocharged, dtype: int64" ] }, - "execution_count": 85, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -7477,7 +7762,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -7489,7 +7774,7 @@ "Name: Thumb, dtype: int64" ] }, - "execution_count": 86, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -7501,7 +7786,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -7515,7 +7800,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.9.16" } }, "nbformat": 4,