Allena il regressore GB

Ora allenerai il regressore a gradient boosting gb che hai istanziato nell’esercizio precedente e ne userai le predizioni sul set di test.

Il dataset è suddiviso in 80% train e 20% test. Le matrici delle feature X_train e X_test, così come gli array y_train e y_test, sono disponibili nel tuo workspace. Inoltre, abbiamo già caricato l’istanza del modello gb che hai definito nell’esercizio precedente.

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

Visualizza il corso

Istruzioni dell'esercizio

Esegui il fit di gb sul training set.
Predici le etichette del test set e assegna il risultato a y_pred.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Fit gb to the training set
____

# Predict test set labels
y_pred = ____

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: Definisci il classificatore AdaBoost Exercise 3: Allena il classificatore AdaBoost Exercise 4: Valuta il classificatore AdaBoost Exercise 5: Gradient Boosting (GB)Exercise 6: Definisci il regressore GB Exercise 7: Allena il regressore GB

Esercizio in corso

Exercise 8: Valuta il regressore GB Exercise 9: Stochastic Gradient Boosting (SGB)Exercise 10: Regressione con SGB Exercise 11: Allena il regressore SGB Exercise 12: Valuta il regressore SGB

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!