Update Part 4 Boosting (Adaboost, GB, SGB)

Author: sandipanpaul21

Part 4 Boosting (Adaboost, GB, SGB)

@@ -58,3 +58,33 @@ rmse_test = MSE(y_test, y_pred)**(1/2)
 
 # Print the test set RMSE
 print('Test set RMSE: {:.2f}'.format(rmse_test))
+
+
+# Stochastic Gradient Boosting in sklearn
+# Import models and utility functions
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error as MSE
+
+# Set seed for reproducibility
+SEED = 1
+
+# Split dataset into 70% train and 30% test
+X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3,random_state=SEED)
+
+# Instantiate a stochastic GradientBoostingRegressor 'sgbt'
+sgbt = GradientBoostingRegressor(max_depth=1, subsample=0.8, max_features=0.2, n_estimators=300, random_state=SEED)
+# 0.8 refers to sample 80% of datafor training
+# 0.2 refers to each tree uses 20% of the available features to perform best split
+
+# Fit 'sgbt' to the training set
+sgbt.fit(X_train, y_train)
+
+# Predict the test set labels
+y_pred = sgbt.predict(X_test)
+
+# Evaluate test set RMSE 'rmse_test'
+rmse_test = MSE(y_test, y_pred)**(1/2)
+
+# Print 'rmse_test'
+print('Test set RMSE: {:.2f}'.format(rmse_test))