Demo Gradient Boosting

machine_learning/Gradient_boosting_regressor.py

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+"""Implementation of GradientBoostingRegressor in sklearn using the
+ boston dataset which is very popular for regression problem to
+ predict house price.
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_boston
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.model_selection import train_test_split
+
+
+def main():
+
+ # loading the dataset from the sklearn
+ df = load_boston()
+ print(df.keys())
+ # now let construct a data frame
+ df_boston = pd.DataFrame(df.data, columns=df.feature_names)
+ # let add the target to the dataframe
+ df_boston["Price"] = df.target
+ # print the first five rows using the head function
+ print(df_boston.head())
+ # Summary statistics
+ print(df_boston.describe().T)
+ # Feature selection
+
+ X = df_boston.iloc[:, :-1]
+ y = df_boston.iloc[:, -1] # target variable
+ # split the data with 75% train and 25% test sets.
+ X_train, X_test, y_train, y_test = train_test_split(
+ X, y, random_state=0, test_size=0.25
+ )
+
+ model = GradientBoostingRegressor(
+ n_estimators=500, max_depth=5, min_samples_split=4, learning_rate=0.01
+ )
+ # training the model
+ model.fit(X_train, y_train)
+ """let have a look on the train and test score
+ to see how good the model fit the data"""
+ score = model.score(X_train, y_train).round(3)
+ print("Training score of GradientBoosting is :", score)
+ print(
+ "the test score of GradienBoosting is :",
+ model.score(X_test, y_test).round(3)
+ )
+ # Let us evaluation the model by finding the errors
+ y_pred = model.predict(X_test)
+
+ # The mean squared error
+ print("Mean squared error: %.2f" % mean_squared_error(y_test, y_pred))
+ # Explained variance score: 1 is perfect prediction
+ print("Test Variance score: %.2f" % r2_score(y_test, y_pred))
+
+ # So let's run the model against the test data
+ fig, ax = plt.subplots()
+ ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0))
+ ax.plot([y_test.min(), y_test.max()],
+ [y_test.min(), y_test.max()], "k--", lw=4)
+ ax.set_xlabel("Actual")
+ ax.set_ylabel("Predicted")
+ ax.set_title("Truth vs Predicted")
+ # this show function will display the plotting
+ plt.show()
+
+
+if __name__ == "__main__":
+ main()