Evalueer de 10-voudige CV-fout

In deze oefening evalueer je de 10-voudige CV Root Mean Squared Error (RMSE) behaald door de regressieboom dt die je in de vorige oefening hebt aangemaakt.

Naast dt zijn ook de trainingsgegevens X_train en y_train beschikbaar in je werkruimte. We hebben ook cross_val_score uit sklearn.model_selection geïmporteerd.

Let op: omdat cross_val_score alleen de mogelijkheid heeft om negatieve MSE’s te evalueren, moet de uitvoer met min één worden vermenigvuldigd om de MSE’s te krijgen. De CV-RMSE kun je vervolgens verkrijgen door de vierkantswortel van de gemiddelde MSE te nemen.

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Machine Learning met boomgebaseerde modellen in Python

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Oefeninstructies

Bereken de 10-voudig cross-gevalideerde MSE van dt door het argument scoring in te stellen op 'neg_mean_squared_error'.
Bereken de RMSE op basis van de verkregen MSE-scores.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Compute the array containing the 10-folds CV MSEs
MSE_CV_scores = - ____(____, ____, ____, cv=____, 
                       ____='____',
                       n_jobs=-1)

# Compute the 10-folds CV RMSE
RMSE_CV = (____.____)**(____)

# Print RMSE_CV
print('CV RMSE: {:.2f}'.format(RMSE_CV))

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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

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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

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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

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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

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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!