Appeler les choses par leur nom

Le surapprentissage et le sous-apprentissage mènent tous deux à de mauvaises performances sur les données de test, mais leurs implications diffèrent. Dans cet exercice, vous allez classer des caractéristiques de modèles comme symptômes de surapprentissage ou de sous-apprentissage afin de mieux comprendre le compromis biais-variance.

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<cours>Machine Learning avec des modèles à base d’arbres en R</cours>

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Cet exercice fait partie du cours

<cours>Machine Learning avec des modèles à base d’arbres en R</cours>

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Ready to build a real machine learning pipeline? Complete step-by-step exercises to learn how to create decision trees, split your data, and predict which patients are most likely to suffer from diabetes. Last but not least, you’ll build performance measures to assess your models and judge your predictions.

Exercise 1: Welcome to the course!Exercise 2: Why tree-based methods?Exercise 3: Specify that tree Exercise 4: Train that model Exercise 5: How to grow your tree Exercise 6: Train/test split Exercise 7: Avoiding class imbalances Exercise 8: From zero to hero Exercise 9: Predict and evaluate Exercise 10: Make predictions Exercise 11: Crack the matrix Exercise 12: Are you predicting correctly?

Ready for some candy? Use a chocolate rating dataset to build regression trees and assess their performance using suitable error measures. You’ll overcome statistical insecurities of single train/test splits by applying sweet techniques like cross-validation and then dive even deeper by mastering the bias-variance tradeoff.

Exercise 1: Variables continues Exercise 2: Entraîner un arbre de régression Exercise 3: Prédire de nouvelles valeurs Exercise 4: Examiner la sortie du modèle Exercise 5: Mesures de performance pour les arbres de régression Exercise 6: Performance sur l’échantillon d’entraînement Exercise 7: Performance hors échantillon Exercise 8: Plus l’erreur est grande, plus la pénalité est forte Exercise 9: Validation croisée Exercise 10: Créer les plis Exercise 11: Ajuster les folds Exercise 12: Évaluer les plis Exercise 13: Compromis biais-variance Exercise 14: Appeler les choses par leur nom

Exercice actuel

Exercise 15: Ajuster la complexité du modèle Exercise 16: Performances en échantillon et hors échantillon

Time to get serious with tuning your hyperparameters and interpreting receiver operating characteristic (ROC) curves. In this chapter, you’ll leverage the wisdom of the crowd with ensemble models like bagging or random forests and build ensembles that forecast which credit card customers are most likely to churn.

Exercise 1: Tuning hyperparameters Exercise 2: Generate a tuning grid Exercise 3: Tune along the grid Exercise 4: Pick the winner Exercise 5: More model measures Exercise 6: Calculate specificity Exercise 7: Draw the ROC curve Exercise 8: Area under the ROC curve Exercise 9: Bagged trees Exercise 10: Create bagged trees Exercise 11: In-sample ROC and AUC Exercise 12: Check for overfitting Exercise 13: Random forest Exercise 14: Bagged trees vs. random forest Exercise 15: Variable importance

Ready for the high society of tree-based models? Apply gradient boosting to create powerful ensembles that perform better than anything that you have seen or built. Learn about their fine-tuning and how to compare different models to pick a winner for production.

Exercise 1: Introduction to boosting Exercise 2: Bagging vs. boosting Exercise 3: Specify a boosted ensemble Exercise 4: Gradient boosting Exercise 5: Train a boosted ensemble Exercise 6: Evaluate the ensemble Exercise 7: Compare to a single classifier Exercise 8: Optimize the boosted ensemble Exercise 9: Tuning preparation Exercise 10: The actual tuning Exercise 11: Finalize the model Exercise 12: Model comparison Exercise 13: Compare AUC Exercise 14: Plot ROC curves Exercise 15: Wrap-up