Evalueer de classificatieboom

Nu je je eerste classificatieboom hebt getraind, is het tijd om de prestaties op de testset te evalueren. Dat doe je met de metric accuracy, die overeenkomt met het aandeel correcte voorspellingen op de testset.

Het getrainde model dt uit de vorige oefening is geladen in je werkruimte, samen met de feature-matrix van de testset X_test en de array met labels y_test.

Deze oefening maakt deel uit van de cursus

Machine Learning met boomgebaseerde modellen in Python

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Oefeninstructies

Importeer de functie accuracy_score uit sklearn.metrics.
Voorspel de labels van de testset en ken de verkregen array toe aan y_pred.
Evalueer de accuracy op de testset van dt door accuracy_score() aan te roepen en ken de waarde toe aan acc.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import accuracy_score
from ____.____ import ____

# Predict test set labels
y_pred = ____.____(____)

# Compute test set accuracy  
acc = ____(____, ____)
print("Test set accuracy: {:.2f}".format(acc))

Code bewerken en uitvoeren

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: Beslissingsboom voor classificatie Exercise 2: Train je eerste classificatieboom Exercise 3: Evalueer de classificatieboom

Huidige oefening

Exercise 4: Logistische regressie vs. classificatieboom Exercise 5: Classification tree leren Exercise 6: Een classificatieboom laten groeien Exercise 7: Entropie als criterium gebruiken Exercise 8: Entropie vs. Gini-index Exercise 9: Beslissingsboom voor regressie Exercise 10: Train je eerste regressieboom Exercise 11: Evalueer de regressieboom Exercise 12: Lineaire regressie vs regressieboom

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: 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!