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import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
Load the boston dataset:
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from sklearn.datasets import load_boston
boston = load_boston()
boston.keys()
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print(boston.DESCR)
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boston.data.shape
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boston.target.shape
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boston.target
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from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)
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from sklearn.linear_model import Ridge
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ridge = Ridge()
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ridge.fit(X_train, y_train)
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pred_test = ridge.predict(X_test)
pred_test
R2 score:
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ridge.score(X_test, y_test)
MSE:
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from sklearn.metrics import mean_squared_error
mean_squared_error(y_test, pred_test)
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from sklearn.ensemble import RandomForestRegressor
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rf = RandomForestRegressor()
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rf.fit(X_train, y_train)
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rf.score(X_test, y_test)
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mean_squared_error(y_test, rf.predict(X_test))