Gradient boosting regressor

Author: abdoulayegk

machine_learning/Gradient-boosting-regressor.py

@@ -2,64 +2,70 @@
    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, RandomForestRegressor
+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 package
     df = load_boston()
     print(df.keys())
     # now let construct a data frame with data and target variables
-    df_boston = pd.DataFrame(df.data,columns =df.feature_names)
+    df_boston = pd.DataFrame(df.data, columns=df.feature_names)
     # let add the target to the dataframe
-    df_boston['Price']= df.target
+    df_boston["Price"] = df.target
     # let us print the first five rows using the head function
     print(df_boston.head())
-    print(df_boston.describe().T) # to see summary statistics of the dataset
+    print(df_boston.describe().T)  # to see summary statistics of the dataset
     # Feature selection means for independent and dependent variables
-    X = df_boston.iloc[:,:-1]
-    y = df_boston.iloc[:,-1] # target variable
+
+    X = df_boston.iloc[:, :-1]
+    y = df_boston.iloc[:, -1]  # target variable
     # we are going to 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 = .25)
-    # now let set the parameters of the model
-    params = {'n_estimators': 500, 'max_depth': 5, 'min_samples_split': 4,
-          'learning_rate': 0.01, 'loss': 'ls'}
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, random_state=0, test_size=0.25
+    )
+    # model parameter
+    params = {
+        "n_estimators": 500,
+        "max_depth": 5,
+        "min_samples_split": 4,
+        "learning_rate": 0.01,
+        "loss": "ls",
+    }
     model = GradientBoostingRegressor(**params)
     # 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 
+    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))
+    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))
-    
+    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.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 
+    # this show function will display the plotting
     plt.show()
-    
-
-if __name__ =='__main__':
-    main()
-
-
-# In[ ]:
-
-
 
 
+if __name__ == "__main__":
+    main()