Evaluate the optimal tree

In this exercise, you'll evaluate the test set ROC AUC score of grid_dt's optimal model.

In order to do so, you will first determine the probability of obtaining the positive label for each test set observation. You can use the methodpredict_proba() of an sklearn classifier to compute a 2D array containing the probabilities of the negative and positive class-labels respectively along columns.

The dataset is already loaded and processed for you (numerical features are standardized); it is split into 80% train and 20% test. X_test, y_test are available in your workspace. In addition, we have also loaded the trained GridSearchCV object grid_dt that you instantiated in the previous exercise. Note that grid_dt was trained as follows:

grid_dt.fit(X_train, y_train)

This exercise is part of the course

Machine Learning with Tree-Based Models in Python

Exercise instructions

Import roc_auc_score from sklearn.metrics.
Extract the .best_estimator_ attribute from grid_dt and assign it to best_model.
Predict the test set probabilities of obtaining the positive class y_pred_proba.
Compute the test set ROC AUC score test_roc_auc of best_model.

Hands-on interactive exercise

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

# Import roc_auc_score from sklearn.metrics
____

# Extract the best estimator
best_model = ____

# Predict the test set probabilities of the positive class
y_pred_proba = ____

# Compute test_roc_auc
test_roc_auc = ____

# Print test_roc_auc
print('Test set ROC AUC score: {:.3f}'.format(test_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.

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

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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 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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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

Current Exercise

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!