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41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
# Random Forest Regressor Example
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from sklearn.datasets import load_boston
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.metrics import mean_absolute_error, mean_squared_error
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from sklearn.model_selection import train_test_split
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def main():
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"""
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Random Forest Regressor Example using sklearn function.
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Boston house price dataset is used to demonstrate the algorithm.
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"""
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# Load Boston house price dataset
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boston = load_boston()
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print(boston.keys())
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# Split dataset into train and test data
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X = boston["data"] # features
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Y = boston["target"]
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x_train, x_test, y_train, y_test = train_test_split(
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X, Y, test_size=0.3, random_state=1
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)
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# Random Forest Regressor
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rand_for = RandomForestRegressor(random_state=42, n_estimators=300)
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rand_for.fit(x_train, y_train)
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# Predict target for test data
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predictions = rand_for.predict(x_test)
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predictions = predictions.reshape(len(predictions), 1)
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# Error printing
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print(f"Mean Absolute Error:\t {mean_absolute_error(y_test, predictions)}")
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print(f"Mean Square Error :\t {mean_squared_error(y_test, predictions)}")
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if __name__ == "__main__":
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main()
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