Betere prestaties met een Voting Classifier

Tot slot ga je de prestaties evalueren van een voting classifier die de outputs gebruikt van de modellen in de lijst classifiers en labels toekent via meerderheid van stemmen.

X_train, X_test, y_train, y_test, de lijst classifiers uit een eerdere oefening, en de functie accuracy_score uit sklearn.metrics 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 VotingClassifier uit sklearn.ensemble.
Instantieer een VotingClassifier door de parameter estimators op classifiers te zetten en wijs deze toe aan vc.
Fit vc op de trainingsset.
Evalueer de nauwkeurigheid van vc op de testset met de testsetvoorspellingen y_pred.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import VotingClassifier from sklearn.ensemble
____

# Instantiate a VotingClassifier vc
vc = ____(estimators=____)     

# Fit vc to the training set
____.____(____, ____)   

# Evaluate the test set predictions
y_pred = vc.predict(X_test)

# Calculate accuracy score
accuracy = ____(____, ____)
print('Voting Classifier: {:.3f}'.format(accuracy))

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Deze oefening maakt deel uit van de cursus

Machine Learning met boomgebaseerde modellen in Python

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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: Generalisatiefout Exercise 2: Complexiteit, bias en variantie Exercise 3: Overfitting en underfitting Exercise 4: Diagnoseer bias- en variantieproblemen Exercise 5: Het model instantiëren Exercise 6: Evalueer de 10-voudige CV-fout Exercise 7: Evalueer de trainingsfout Exercise 8: Hoge bias of hoge variance?Exercise 9: Ensemble learning Exercise 10: Definieer het ensemble Exercise 11: Beoordeel afzonderlijke classificatiemodellen Exercise 12: Betere prestaties met een Voting Classifier

Huidige oefening

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!