Jun_ML_Linear_Regression_HW_7.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Jun_ML_Linear_Regression_HW_7.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/oObelix/DataScience/blob/master/Jun_ML_Linear_Regression_HW_7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kj4RcGot8CZV"
      },
      "source": [
        "### Урок 7. Домашняя работа\n",
        "\n",
        "У вас есть два набора точек – истинные значения *y_true* и предсказанные значения *y_pred*\n",
        "\n",
        "* для каждой точки из *y_true* постройте величину ошибки $e = y-\\hat{y}$ – это называется **остатки регрессии**\n",
        "* возведите ошибки в квадрат $e^2$\n",
        "* постройте график ошибок – зависимость $e^2$ от $e$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YS5vFeVt8CZb"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "y_pred_ = np.array([30.0, 25.03, 30.57, 28.61, 27.94, 25.26, 23.0, 19.54, 11.52, 18.92, 19.0, 21.59, 20.91, 19.55, 19.28, 19.3, 20.53, 16.91, 16.18, 18.41, 12.52, 17.67, 15.83, 13.81, 15.68, 13.39, 15.46, 14.71, 19.55, 20.88, 11.46, 18.06, 8.81, 14.28, 13.71, 23.81, 22.34, 23.11, 22.92, 31.36])\n",
        "y_true_ = np.array([24.0, 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15.0, 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6, 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21.0, 12.7, 14.5, 13.2, 13.1, 13.5, 18.9, 20.0, 21.0, 24.7, 30.8])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Msa1g5qv8CZq"
      },
      "source": [
        "График должен получится вот такой:\n",
        "\n",
        "![experiment_errors](https://sun9-50.userapi.com/c856120/v856120638/1e9be0/3m-6-p2kZfk.jpg)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkZUITeD8CZt"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "# -- ВАШ КОД ТУТ --\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvAZSGGXFxTj",
        "outputId": "d1376ac7-f058-4de4-82c4-493c8f49a221"
      },
      "source": [
        "y_error = y_true_ - y_pred_\n",
        "y_error"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-6.  , -3.43,  4.13,  4.79,  8.26,  3.44, -0.1 ,  7.56,  4.98,\n",
              "       -0.02, -4.  , -2.69,  0.79,  0.85, -1.08,  0.6 ,  2.57,  0.59,\n",
              "        4.02, -0.21,  1.08,  1.93, -0.63,  0.69, -0.08,  0.51,  1.14,\n",
              "        0.09, -1.15,  0.12,  1.24, -3.56,  4.39, -1.18, -0.21, -4.91,\n",
              "       -2.34, -2.11,  1.78, -0.56])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ykNLBTOCuVN",
        "outputId": "ba9feaca-231c-4a03-c06d-0738cb09bf31"
      },
      "source": [
        "y_error_square = y_error ** 2\n",
        "y_error_square"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.60000e+01, 1.17649e+01, 1.70569e+01, 2.29441e+01, 6.82276e+01,\n",
              "       1.18336e+01, 1.00000e-02, 5.71536e+01, 2.48004e+01, 4.00000e-04,\n",
              "       1.60000e+01, 7.23610e+00, 6.24100e-01, 7.22500e-01, 1.16640e+00,\n",
              "       3.60000e-01, 6.60490e+00, 3.48100e-01, 1.61604e+01, 4.41000e-02,\n",
              "       1.16640e+00, 3.72490e+00, 3.96900e-01, 4.76100e-01, 6.40000e-03,\n",
              "       2.60100e-01, 1.29960e+00, 8.10000e-03, 1.32250e+00, 1.44000e-02,\n",
              "       1.53760e+00, 1.26736e+01, 1.92721e+01, 1.39240e+00, 4.41000e-02,\n",
              "       2.41081e+01, 5.47560e+00, 4.45210e+00, 3.16840e+00, 3.13600e-01])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "MCSZd0QBF1yW",
        "outputId": "7d53ff7f-494e-4d62-e8d3-f9bcd8299bf1"
      },
      "source": [
        "plt.scatter(y_error, y_error_square)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L1NLD85vuetRZDy20cBVlvX9TTxirU2yhbBDe+LZ1Fkbdyx3VgEc26gxH4BuEsx5ap940zaU8slEnGOAbiLMeWmO10bVHNuoUA1yqNLo64EonhYc9slEHGeBas4mpaX7vvzzFK6/OAzA40M/v3v5TRYXX4pORi6darjSbZLmrKD/7wWuL+m9X+TyJqTWZmJpm/OCTZ8MbYHZunk888ASfnjjWxcoa9+mJY/zmA0+cHU03Ok9+bPswn/3gtQwPDhDURt2Gt7rBEbjWZN/hE8yfWRp5Nfc99gKjP3ZpzwTaxNQ0v/vwU8zO1X7ZXHJxP+//6S3c99gL54X2Usv1uz2foF5ggGtNVjqRl1B3HnS9IP03/7R1LZeV5mYv9sqr83zxsRca+judTaJetq4Aj4jbgLuBPuCezPTu9BvESify4PyAn5iaZvzLTzL/xptj3ldenWf84JPAm33mT08c4/6j3+VMnj82XtyjXhr+S6+IbAVnk6jXrbkHHhF9wH8Efg64BvhIRFzTqsLU28Z3jtDfF8s+v3Tkuu/wiXPCe8H8mTzbZ/70xDG++NgLdcMbzu1RL4T/wrojjczNboZ9bZVgPSPwG4BnM/M5gIj4Y+AO4OlWFKbethBsn3rwGD947dzgrDdyXanlsvDc/Ue/21QNC+E/tn14TVc+Ll3gK4CP3nglnxm7tum/S+qG9cxCGQYW/8S9WO07R0TsjojJiJicmZlZx8ep14xtH+apf3sbn/+F61edkbFSL3nhueVG3itZCO5me9X9fcFHb7zynLo/9wvXG94qSttPYmbmfmA/wOjoaPM/oep5jczIGN85cl4PHGpBujBa74toOsQXgrve3OzltPrkqdQt6wnwaeCKRduXV/uk8yyE5UqzUD7yD69oeHYInBv+i9d6qXdy1SskdSGKXMNhK0BEbAL+N7CDWnB/E/jFzHxqufeMjo7m5OTkmj5PG8NaZ6FIF7KIeDwzR5fuX/MIPDNfj4h/DhymNo3wCyuFt9SIz4xdax9aatC6euCZ+afAn7aoFklSE1wLRZIKZYBLUqEMcEkqlAEuSYVa8zTCNX1YxAzw/Brffhnw1y0sp51KqbWUOsFa26GUOsFafywzh5bu7GiAr0dETNabB9mLSqm1lDrBWtuhlDrBWpdjC0WSCmWAS1KhSgrw/d0uoAml1FpKnWCt7VBKnWCtdRXTA5cknaukEbgkaREDXJIKVVyAR8THIuI7EfFURPy7btezmoj4ZERkRFzW7VrqiYh91b/nX0TEgxEx2O2aloqI2yLiREQ8GxF7ul1PPRFxRUR8PSKerr43P97tmlYTEX0RMRURX+l2LSuJiMGIOFh9nx6PiHd1u6Z6IuI3q//3346I+yPiLe3+zKICPCLeQ+2+m9dl5k8B/77LJa0oIq4A3gs0fpeCznsEeEdm/jS19d33drmecxR08+zXgU9m5jXAjcBv9Gidi30cON7tIhpwN/DVzHw7cB09WHNEDAP/AhjNzHdQW2L7w+3+3KICHPh14K7M/FuAzDzd5XpW8zngtzj33rk9JTO/lpmvV5uPUbuzUi85e/PszHwNWLh5dk/JzFOZ+a3q8fephUzP3m0iIi4H3g/c0+1aVhIRbwN+FrgXIDNfy8zZ7la1rE3AQHWzm4uBk+3+wNIC/CeBfxIRRyPif0TEz3S7oOVExB3AdGY+2e1amvCrwH/rdhFLNHTz7F4SEduA7cDR7layos9TG1y80e1CVnEVMAP8YdXuuSci3trtopbKzGlqHYEXgFPA/83Mr7X7c9t+U+NmRcR/B/5+nac+Ra3eS6kdov4M8KWI+PHs0lzIVWr9HWrtk65bqc7MfKh6zaeotQHu62RtF5qI+CHgT4BPZOb3ul1PPRHxAeB0Zj4eEe/udj2r2AS8E/hYZh6NiLuBPcC/6m5Z54qIS6gdGV4FzAJfjohfyswvtvNzey7AM/OW5Z6LiF8HDlWB/ecR8Qa1hWNmOlXfYsvVGhHXUvsf+WREQK0t8a2IuCEzX+pgicDK/6YAEfHPgA8AO7r1y3AFxdw8OyL6qYX3fZl5qNv1rOAm4PaIeB/wFuCHI+KLmflLXa6rnheBFzNz4WjmILUA7zW3AP8nM2cAIuIQ8I+AtgZ4aS2UCeA9ABHxk8BF9OAKZZl5LDN/NDO3ZeY2at+E7+xGeK8mIm6jdih9e2a+2u166vgmcHVEXBURF1E7MfRwl2s6T9R+U98LHM/MP+h2PSvJzL2ZeXn1vflh4NEeDW+qn5nvRsRItWsH8HQXS1rOC8CNEXFx9b2wgw6cbO25EfgqvgB8ISK+DbwG7OrBEWNp/gPwd4FHqqOFxzLz17pb0psKunn2TcAvA8ci4olq3+9U943V+nwMuK/6Bf4c8Ctdruc8VXvnIPAtaq3IKTpwSb2X0ktSoUproUiSKga4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKtT/B4JTTb/2LqPEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}