Python/machine_learning/random_forest_regressor.py

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