Evalueer de AdaBoost-classifier

Nu je ada hebt getraind en de kansen op de positieve klasse in de testset hebt voorspeld, is het tijd om de ROC AUC-score van ada te evalueren. Denk eraan dat de ROC AUC-score van een binaire classifier kan worden bepaald met de functie roc_auc_score() uit sklearn.metrics.

De arrays y_test en y_pred_proba die je in de vorige oefening hebt berekend, zijn beschikbaar in je werkruimte.

Deze oefening maakt deel uit van de cursus

Machine Learning met boomgebaseerde modellen in Python

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Oefeninstructies

Importeer roc_auc_score uit sklearn.metrics.
Bereken de ROC AUC-score op de testset voor ada, wijs deze toe aan ada_roc_auc, en print hem.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import roc_auc_score
____

# Evaluate test-set roc_auc_score
____ = ____(____, ____)

# Print roc_auc_score
print('ROC AUC score: {:.2f}'.format(ada_roc_auc))

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Deze oefening maakt deel uit van de cursus

Machine Learning met boomgebaseerde modellen 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

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: Definieer de AdaBoost-classifier Exercise 3: Train de AdaBoost-classifier Exercise 4: Evalueer de AdaBoost-classifier

Huidige oefening

Exercise 5: Gradient Boosting (GB)Exercise 6: Definieer de GB-regressor Exercise 7: Train de GB-regressor Exercise 8: Evalueer de GB-regressor Exercise 9: Stochastic Gradient Boosting (SGB)Exercise 10: Regressie met SGB Exercise 11: Train de SGB-regressor Exercise 12: Evalueer de 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!