Evalueer de optimale boom

In deze oefening evalueer je de ROC AUC-score op de testset van het optimale model van grid_dt.

Om dit te doen bepaal je eerst voor elke observatie in de testset de kans op het positieve label. Je kunt de methode predict_proba() van een sklearn-classifier gebruiken om een 2D-array te berekenen met daarin de waarschijnlijkheden van respectievelijk de negatieve en positieve klasselabels als kolommen.

De gegevensset is al voor je geladen en verwerkt (numerieke kenmerken zijn gestandaardiseerd); de data is gesplitst in 80% training en 20% test. X_test, y_test zijn beschikbaar in je werkruimte. Daarnaast hebben we ook het getrainde GridSearchCV-object grid_dt geladen dat je in de vorige oefening hebt aangemaakt. Merk op dat grid_dt als volgt is getraind:

grid_dt.fit(X_train, y_train)

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.
Haal het attribuut .best_estimator_ uit grid_dt en wijs het toe aan best_model.
Voorspel de testset-waarschijnlijkheden op de positieve klasse als y_pred_proba.
Bereken de ROC AUC-score op de testset test_roc_auc van best_model.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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))

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Machine Learning met boomgebaseerde modellen in Python

SkillTag.level.intermediateSkillTag.label

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Begin de cursus 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: 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: De hyperparameters van een CART afstemmen Exercise 2: Hyperparameters van bomen Exercise 3: Stel het hyperparameterrooster van de boom in Exercise 4: Zoek naar de optimale boom Exercise 5: Evalueer de optimale boom

Huidige oefening

Exercise 6: De hyperparameters van een RF afstemmen Exercise 7: Hyperparameters van random forests Exercise 8: Stel het hyperparameterrooster van RF in Exercise 9: Zoek naar het optimale bos Exercise 10: Evalueer het optimale bos Exercise 11: Gefeliciteerd!