OOB-score vs. testset-score

Nu je bc hebt geïnstantieerd, ga je het model fitten op de trainingsset en de nauwkeurigheid op zowel de testset als de OOB-schatting evalueren.

De gegevensset is al voor je verwerkt en gesplitst in 80% train en 20% test. De feature-matrices X_train en X_test, en de label-arrays y_train en y_test zijn beschikbaar in je werkruimte. Daarnaast hebben we de classifier bc (geïnstantieerd in de vorige oefening) en de functie accuracy_score() uit sklearn.metrics al geladen.

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Machine Learning met boomgebaseerde modellen in Python

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Oefeninstructies

Fit bc op de trainingsset, voorspel de labels voor de testset en sla de resultaten op in y_pred.
Evalueer de testset-nauwkeurigheid acc_test door accuracy_score aan te roepen.
Evalueer de OOB-nauwkeurigheid acc_oob van bc door het attribuut oob_score_ uit bc te halen.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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

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

# Evaluate test set accuracy
acc_test = ____(____, ____)

# Evaluate OOB accuracy
acc_oob = ____.____

# Print acc_test and acc_oob
print('Test set accuracy: {:.3f}, OOB accuracy: {:.3f}'.format(acc_test, acc_oob))

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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: 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: Definieer de bagging-classifier Exercise 3: Prestaties van Bagging evalueren Exercise 4: Out-of-bag-evaluatie Exercise 5: Voorbereiden Exercise 6: OOB-score vs. testset-score

Huidige oefening

Exercise 7: Random Forests (RF)Exercise 8: Train een RF-regressor Exercise 9: Evalueer de RF-regressor Exercise 10: Feature-importances visualiseren

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!