Visualizzare l'importanza delle feature

In questo esercizio determinerai quali feature sono state le più predittive secondo il random forests regressor rf che hai addestrato in un esercizio precedente.

A questo scopo, disegnerai un grafico a barre orizzontali delle importanze delle feature così come valutate da rf. Per fortuna, si può fare facilmente grazie alle funzionalità di plotting di pandas.

Abbiamo creato un oggetto pandas.Series chiamato importances che contiene i nomi delle feature come index e le rispettive importanze come valori. Inoltre, matplotlib.pyplot è disponibile come plt e pandas come pd.

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Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Chiama il metodo .sort_values() su importances e assegna il risultato a importances_sorted.
Chiama il metodo .plot() su importances_sorted e imposta gli argomenti:
- kind a 'barh'
- color a 'lightgreen'

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create a pd.Series of features importances
importances = pd.Series(data=rf.feature_importances_,
                        index= X_train.columns)

# Sort importances
importances_sorted = ____

# Draw a horizontal barplot of importances_sorted
____.____(____='____', ____='____')
plt.title('Features Importances')
plt.show()

Modifica ed esegui il codice

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

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: 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 Exercise 10: Visualizzare l'importanza delle feature

Esercizio in corso

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!