Valuta l'errore di CV a 10 fold

In questo esercizio valuterai la Root Mean Squared Error (RMSE) convalidata incrociata a 10 fold ottenuta dall'albero di regressione dt che hai istanziato nell'esercizio precedente.

Oltre a dt, nel tuo workspace sono disponibili i dati di training, inclusi X_train e y_train. Abbiamo anche importato cross_val_score da sklearn.model_selection.

Nota che, poiché cross_val_score consente solo di valutare MSE negative, il suo output deve essere moltiplicato per meno uno per ottenere le MSE. La RMSE di CV può quindi essere ottenuta calcolando la radice quadrata della MSE media.

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

Visualizza il corso

Istruzioni dell'esercizio

Calcola la MSE convalidata incrociata a 10 fold di dt impostando l'argomento scoring a 'neg_mean_squared_error'.
Calcola la RMSE a partire dai punteggi di MSE ottenuti.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Compute the array containing the 10-folds CV MSEs
MSE_CV_scores = - ____(____, ____, ____, cv=____, 
                       ____='____',
                       n_jobs=-1)

# Compute the 10-folds CV RMSE
RMSE_CV = (____.____)**(____)

# Print RMSE_CV
print('CV RMSE: {:.2f}'.format(RMSE_CV))

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: Errore di generalizzazione Exercise 2: Complessità, bias e varianza Exercise 3: Overfitting e underfitting Exercise 4: Diagnostica i problemi di bias e varianza Exercise 5: Istanzia il modello Exercise 6: Valuta l'errore di CV a 10 fold

Esercizio in corso

Exercise 7: Valutare l'errore di training Exercise 8: Bias alto o varianza alta?Exercise 9: Ensemble Learning Exercise 10: Definisci l'ensemble Exercise 11: Valuta i singoli classificatori Exercise 12: Prestazioni migliori con un 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: 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!