INNER CODE UNIT · Python

train_and_fit_random_forest_regressor

alteryx/open_source_demos · predict-daily-temperature/utils.py:86

def train_and_fit_random_forest_regressor(X_train, y_train, X_test, y_test):
    reg = RandomForestRegressor(n_estimators=100)
    reg.fit(X_train, y_train)

    # Check the accuracy of our model
    preds = reg.predict(X_test)
    score = sklearn.metrics.median_absolute_error(preds, y_test)
    print('Median Abs Error: {:.2f}'.format(score))
    return reg, score


def graph_preds_mean_and_y(preds, rolling_mean, y):
    plt.plot(y, color='gray',label='Target')
    plt.plot(rolling_mean, color='blue', label='Rolling Mean')
    plt.plot(preds, color='red', label = 'Predictions')
    plt.legend(loc='best')
    plt.xlabel("Date")
    plt.ylabel("Temperature (C)")

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