Valuta il regressore RF

Ora valuterai la RMSE sul test set del regressore random forests rf che hai addestrato nell’esercizio precedente.

Il dataset è già stato preparato e suddiviso in 80% train e 20% test. La matrice delle caratteristiche X_test e l’array y_test sono disponibili nel tuo workspace. Inoltre, abbiamo caricato anche il modello rf che hai addestrato nell’esercizio precedente.

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Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Importa mean_squared_error da sklearn.metrics come MSE.
Predici le etichette del test set e assegna il risultato a y_pred.
Calcola la RMSE sul test set e assegnala a rmse_test.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import mean_squared_error as MSE
from ____.____ import ____ as ____

# Predict the test set labels
y_pred = ____.____(____)

# Evaluate the test set RMSE
rmse_test = ____

# Print rmse_test
print('Test set RMSE of rf: {:.2f}'.format(rmse_test))

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Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

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Classification and Regression Trees (CART) are a set of supervised learning models used for problems involving classification and regression. In this chapter, you'll be introduced to the CART algorithm.

Exercise 1: Decision tree for classification Exercise 2: Train your first classification tree Exercise 3: Evaluate the classification tree Exercise 4: Logistic regression vs classification tree Exercise 5: Classification tree Learning Exercise 6: Growing a classification tree Exercise 7: Using entropy as a criterion Exercise 8: Entropy vs Gini index Exercise 9: Decision tree for regression Exercise 10: Train your first regression tree Exercise 11: Evaluate the regression tree Exercise 12: Linear regression vs regression tree

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Exercise 1: Generalization Error Exercise 2: Complexity, bias and variance Exercise 3: Overfitting and underfitting Exercise 4: Diagnose bias and variance problems Exercise 5: Instantiate the model Exercise 6: Evaluate the 10-fold CV error Exercise 7: Evaluate the training error Exercise 8: High bias or high variance?Exercise 9: Ensemble Learning Exercise 10: Define the ensemble Exercise 11: Evaluate individual classifiers Exercise 12: Better performance with a Voting Classifier

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Exercise 1: Bagging Exercise 2: Definisci il classificatore bagging Exercise 3: Valutare le prestazioni del Bagging Exercise 4: Valutazione Out-of-Bag Exercise 5: Prepara il terreno Exercise 6: Punteggio OOB vs punteggio sul test set Exercise 7: Random Forests (RF)Exercise 8: Allena un regressore RF Exercise 9: Valuta il regressore RF

Esercizio in corso

Exercise 10: Visualizzare l'importanza delle feature

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Exercise 1: Adaboost Exercise 2: Define the AdaBoost classifier Exercise 3: Train the AdaBoost classifier Exercise 4: Evaluate the AdaBoost classifier Exercise 5: Gradient Boosting (GB)Exercise 6: Define the GB regressor Exercise 7: Train the GB regressor Exercise 8: Evaluate the GB regressor Exercise 9: Stochastic Gradient Boosting (SGB)Exercise 10: Regression with SGB Exercise 11: Train the SGB regressor Exercise 12: Evaluate the SGB regressor

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Exercise 1: Tuning a CART's Hyperparameters Exercise 2: Tree hyperparameters Exercise 3: Set the tree's hyperparameter grid Exercise 4: Search for the optimal tree Exercise 5: Evaluate the optimal tree Exercise 6: Tuning a RF's Hyperparameters Exercise 7: Random forests hyperparameters Exercise 8: Set the hyperparameter grid of RF Exercise 9: Search for the optimal forest Exercise 10: Evaluate the optimal forest Exercise 11: Congratulations!