Machine Learning with Scikit-Learn - 34 - Uncertainty Estimation in MultiClass Datasets.ipynb

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   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "iris = load_iris()  "
   ]
  },
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       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.01, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "              min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "              n_estimators=100, presort='auto', random_state=0,\n",
       "              subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=42)\n",
    "gbrt = GradientBoostingClassifier(learning_rate=0.01, random_state=0)\n",
    "\n",
    "gbrt.fit(X_train, y_train)"
   ]
  },
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   "execution_count": 6,
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     "text": [
      "The decision function for the 3-class iris dataset:\n",
      "\n",
      "[[-0.52931069  1.46560359 -0.50448467]\n",
      " [ 1.51154215 -0.49561142 -0.50310736]\n",
      " [-0.52379401 -0.4676268   1.51953786]\n",
      " [-0.52931069  1.46560359 -0.50448467]\n",
      " [-0.53107259  1.28190451  0.21510024]\n",
      " [ 1.51154215 -0.49561142 -0.50310736]\n",
      " [-0.52931069  1.46560359 -0.50448467]\n",
      " [-0.53043817 -0.4676268   1.51953786]\n",
      " [-0.52931069  1.46560359 -0.50448467]\n",
      " [-0.52931069  1.46560359 -0.50448467]]\n"
     ]
    }
   ],
   "source": [
    "print('The decision function for the 3-class iris dataset:\\n\\n{}'.format(gbrt.decision_function(X_test[:10])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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     "text": [
      "Predicted probabilities for the samples in the iris dataset:\n",
      "\n",
      "[[ 0.10664722  0.7840248   0.10932798]\n",
      " [ 0.78880668  0.10599243  0.10520089]\n",
      " [ 0.10231173  0.10822274  0.78946553]\n",
      " [ 0.10664722  0.7840248   0.10932798]\n",
      " [ 0.10825347  0.66344934  0.22829719]\n",
      " [ 0.78880668  0.10599243  0.10520089]\n",
      " [ 0.10664722  0.7840248   0.10932798]\n",
      " [ 0.10170311  0.10829611  0.79000078]\n",
      " [ 0.10664722  0.7840248   0.10932798]\n",
      " [ 0.10664722  0.7840248   0.10932798]]\n"
     ]
    }
   ],
   "source": [
    "print('Predicted probabilities for the samples in the iris dataset:\\n\\n{}'.format(gbrt.predict_proba(X_test[:10])))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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