Evaluate the AdaBoost classifier

Now that you're done training ada and predicting the probabilities of obtaining the positive class in the test set, it's time to evaluate ada's ROC AUC score. Recall that the ROC AUC score of a binary classifier can be determined using the roc_auc_score() function from sklearn.metrics.

The arrays y_test and y_pred_proba that you computed in the previous exercise are available in your workspace.

This exercise is part of the course

Machine Learning with Tree-Based Models in Python

Exercise instructions

Import roc_auc_score from sklearn.metrics.
Compute ada's test set ROC AUC score, assign it to ada_roc_auc, and print it out.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import roc_auc_score
____

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

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

Edit and Run Code

This exercise is part of the course

Machine Learning with Tree-Based Models in Python

IntermediateSkill Level

4.9+

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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.

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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

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Exercise 1: Adaboost Exercise 2: Define the AdaBoost classifier Exercise 3: Train the AdaBoost classifier Exercise 4: Evaluate the AdaBoost classifier

Current Exercise

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

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