Beoordeel afzonderlijke classificatiemodellen

In deze oefening beoordeel je de prestaties van de modellen in de lijst classifiers die we in de vorige oefening hebben gedefinieerd. Dat doe je door elk model op de trainingsset te fitten en de nauwkeurigheid op de testset te evalueren.

De dataset is al voor je geladen en geprepareerd (numerieke features zijn gestandaardiseerd) en is opgesplitst in 70% train en 30% test. De feature-matrices X_train en X_test, evenals de arrays met labels y_train en y_test, zijn beschikbaar in je werkruimte. Daarnaast hebben we de lijst classifiers uit de vorige oefening geladen, evenals de functie accuracy_score() uit sklearn.metrics.

Deze oefening maakt deel uit van de cursus

Machine Learning met boomgebaseerde modellen in Python

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Oefeninstructies

Loop over de tuples in classifiers. Gebruik clf_name en clf als variabelen in de for-lus:
- Fit clf op de trainingsset.
- Voorspel de labels van de testset met clf en sla de resultaten op in y_pred.
- Evalueer de nauwkeurigheid op de testset van clf en print het resultaat.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Iterate over the pre-defined list of classifiers
for clf_name, clf in ____:    
 
    # Fit clf to the training set
    ____.____(____, ____)    
   
    # Predict y_pred
    y_pred = ____.____(____)
    
    # Calculate accuracy
    accuracy = ____(____, ____) 
   
    # Evaluate clf's accuracy on the test set
    print('{:s} : {:.3f}'.format(clf_name, accuracy))

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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: 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

Huidige oefening

Exercise 12: Betere prestaties met een 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!