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Random Forest Regressor

# Random Forest Regressor Example
from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split

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()

    # 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), 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__":