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random forest algorithm

josh.ipynb answered on June 10, 2022 Popularity 10/10 Helpfulness 3/10

answer random forest algorithm

random forest algorithm

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# 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()

Popularity 10/10 Helpfulness 3/10 Language python

Source: campus.datacamp.com

Tags: algorithm python random-forest

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