4. Select and Train a Mode

Linear Regression

from sklearn.linear_model import LinearRegression

lin_reg = LinearRegression()

lin_reg.fit(housing_prepared, housing_labels)

some_data = housing.iloc[:5]

some_labels = housing_labels.iloc[:5]

some_data_prepared = full_pipeline.transform(some_data)

print("Predictions:", lin_reg.predict(some_data_prepared))

print("Labels:", list(some_labels))

Predictions: [203682.37379543 326371.39370781 204218.64588245 58685.4770482 194213.06443039]

Labels: [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]

print(some_data_prepared)

[[-1.15604281 0.77194962 0.74333089 -0.49323393 -0.44543821 -0.63621141 -0.42069842 -0.61493744 -0.95445595 -0.31205452 -0.08649871 0.15531753 1. 0. 0. 0. 0. ]

[-1.17602483 0.6596948 -1.1653172 -0.90896655 -1.0369278 -0.99833135 -1.02222705 1.33645936 1.89030518 0.21768338 -0.03353391 -0.83628902 1. 0. 0. 0. 0. ]

[ 1.18684903 -1.34218285 0.18664186 -0.31365989 -0.15334458 -0.43363936 -0.0933178 -0.5320456 -0.95445595 -0.46531516 -0.09240499 0.4222004 0. 0. 0. 0. 1. ]

[-0.01706767 0.31357576 -0.29052016 -0.36276217 -0.39675594 0.03604096 -0.38343559 -1.04556555 -0.95445595 -0.07966124 0.08973561 -0.19645314 0. 1. 0. 0. 0. ]

[ 0.49247384 -0.65929936 -0.92673619 1.85619316 2.41221109 2.72415407 2.57097492 -0.44143679 -0.00620224 -0.35783383 -0.00419445 0.2699277 1. 0. 0. 0. 0. ]]

Measure the regression model RMSE on the whole training set using Scikit-Learn's mean_squared_error functions

from sklearn.metrics import mean_squared_error

housing_predictions = lin_reg.predict(housing_prepared)

lin_mse = mean_squared_error(housing_labels, housing_predictions)

lin_rmse = np.sqrt(lin_mse)

print(lin_rmse)

68376.64295459937

Measure the regression model MAE on the whole training set using Scikit-Learn's mean_absolute_error functions

from sklearn.metrics import mean_absolute_error

housing_predictions = lin_reg.predict(housing_prepared)

lin_mae = mean_absolute_error(housing_labels, housing_predictions)

print(lin_mae)

49508.08205970909

Decision Tree

• Use a DecisionTreeRegressor to train the model for comparison purpose

from sklearn.tree import DecisionTreeRegressor

tree_reg = DecisionTreeRegressor(random_state=42)

tree_reg.fit(housing_prepared, housing_labels)

housing_predictions = tree_reg.predict(housing_prepared)

tree_mse = mean_squared_error(housing_labels, housing_predictions)

tree_rmse = np.sqrt(tree_mse)

print(tree_rmse)

0.0 (The model seemed to have badly overfit the data )