Algoritma Hutan Acak
# Import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
# Instantiate rf
rf = RandomForestRegressor(n_estimators=25,
random_state=2)
# Fit rf to the training set
rf.fit(X_train, y_train)
# Import mean_squared_error as MSE
from sklearn.metrics import mean_squared_error as MSE
# Predict the test set labels
y_pred = rf.predict(X_test)
# Evaluate the test set RMSE
rmse_test = MSE(y_test, y_pred)**(1/2)
# Print rmse_test
print('Test set RMSE of rf: {:.2f}'.format(rmse_test))
# Create a pd.Series of features importances
importances = pd.Series(data=rf.feature_importances_,
index= X_train.columns)
# Sort importances
importances_sorted = importances.sort_values()
# Draw a horizontal barplot of importances_sorted
importances_sorted.plot(kind='barh', color='lightgreen')
plt.title('Features Importances')
plt.show()
josh.ipynb