Valuta la foresta ottimale

In questo ultimo esercizio del corso, valuterai la RMSE sul set di test del modello ottimale di grid_rf.

Il dataset è già caricato e preprocessato per te ed è suddiviso in 80% train e 20% test. Nel tuo ambiente sono disponibili X_test, y_test e la funzione mean_squared_error da sklearn.metrics con l'alias MSE. Inoltre, abbiamo anche caricato l'oggetto GridSearchCV addestrato grid_rf che hai istanziato nell'esercizio precedente. Nota che grid_rf è stato addestrato come segue:

grid_rf.fit(X_train, y_train)

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

Visualizza il corso

Istruzioni dell'esercizio

Importa mean_squared_error come MSE da sklearn.metrics.
Estrai il miglior stimatore da grid_rf e assegnalo a best_model.
Predici le etichette del set di test con best_model e assegna il risultato a y_pred.
Calcola la RMSE sul set di test di best_model.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import mean_squared_error from sklearn.metrics as MSE 
____

# Extract the best estimator
best_model = ____

# Predict test set labels
y_pred = ____

# Compute rmse_test
rmse_test = ____

# Print rmse_test
print('Test RMSE of best model: {:.3f}'.format(rmse_test))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

IntermediárioNível de habilidade

4.9+

Inizia il corso gratis

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

The bias-variance tradeoff is one of the fundamental concepts in supervised machine learning. In this chapter, you'll understand how to diagnose the problems of overfitting and underfitting. You'll also be introduced to the concept of ensembling where the predictions of several models are aggregated to produce predictions that are more robust.

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

Bagging is an ensemble method involving training the same algorithm many times using different subsets sampled from the training data. In this chapter, you'll understand how bagging can be used to create a tree ensemble. You'll also learn how the random forests algorithm can lead to further ensemble diversity through randomization at the level of each split in the trees forming the ensemble.

Exercise 1: Bagging Exercise 2: Define the bagging classifier Exercise 3: Evaluate Bagging performance Exercise 4: Out of Bag Evaluation Exercise 5: Prepare the ground Exercise 6: OOB Score vs Test Set Score Exercise 7: Random Forests (RF)Exercise 8: Train an RF regressor Exercise 9: Evaluate the RF regressor Exercise 10: Visualizing features importances

Boosting refers to an ensemble method in which several models are trained sequentially with each model learning from the errors of its predecessors. In this chapter, you'll be introduced to the two boosting methods of AdaBoost and Gradient Boosting.

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

The hyperparameters of a machine learning model are parameters that are not learned from data. They should be set prior to fitting the model to the training set. In this chapter, you'll learn how to tune the hyperparameters of a tree-based model using grid search cross validation.

Exercise 1: Ottimizzare gli iperparametri di un CART Exercise 2: Iperparametri dell'albero Exercise 3: Imposta la griglia di iperparametri dell'albero Exercise 4: Cerca l'albero ottimale Exercise 5: Valuta l’albero ottimale Exercise 6: Ottimizzare gli iperparametri di una RF Exercise 7: Iperparametri delle Random Forest Exercise 8: Imposta la griglia di iperparametri di RF Exercise 9: Cerca la foresta ottimale Exercise 10: Valuta la foresta ottimale

Esercizio in corso

Exercise 11: Congratulazioni!