Fix some stuff

demo/guide-python/sklearn_examples.py

@@ -11,7 +11,7 @@ import xgboost as xgb
 import numpy as np
 from sklearn.cross_validation import KFold
 from sklearn.grid_search import GridSearchCV
-from sklearn.metrics import confusion_matrix
+from sklearn.metrics import confusion_matrix, mean_squared_error
 from sklearn.datasets import load_iris, load_digits, load_boston
 
 rng = np.random.RandomState(31337)
@@ -39,4 +39,26 @@ for train_index, test_index in kf:
     actuals = y[test_index]
     print(confusion_matrix(actuals, predictions))
 
+print("Boston Housing: regression")
+boston = load_boston()
+y = boston['target']
+X = boston['data']
+kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
+for train_index, test_index in kf:
+    xgb_model = xgb.XGBRegressor().fit(X[train_index],y[train_index])
+    predictions = xgb_model.predict(X[test_index])
+    actuals = y[test_index]
+    print(mean_squared_error(actuals, predictions))
+
+print("Parameter optimization")
+y = boston['target']
+X = boston['data']
+xgb_model = xgb.XGBRegressor()
+clf = GridSearchCV(xgb_model,
+                   {'max_depth': [2,4,6],
+                    'n_estimators': [50,100,200]}, verbose=1)
+clf.fit(X,y)
+print(clf.best_score_)
+print(clf.best_params_)
+