Controleer op overfitting

Een heel hoge in-sample AUC, zoals \(99.9\%\), kan wijzen op overfitting. Het kan ook dat je gegevensset gewoon heel goed is gestructureerd, of dat je model simpelweg geweldig is!

Om te controleren wat hier aan de hand is, heb je out-of-sample schattingen van je AUC nodig. Omdat je je testset nog niet wilt gebruiken, kun je die verkrijgen met cross-validation op je trainingsset.

Je trainingsgegevens, customers_train, en de bagged tree-specificatie, spec_bagged, zijn nog beschikbaar in je werkruimte.

Deze oefening maakt deel uit van de cursus

Machine Learning met boomgebaseerde modellen in R

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Oefeninstructies

Gebruik fit_resamples() om je roc_auc-metriek te schatten met drie CV-folds van je trainingsset en de modelformule still_customer ~ total_trans_amt + customer_age + education_level.
Verzamel de metrieken van het resultaat om de AUC te tonen.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

set.seed(55)

# Estimate AUC using cross-validation
cv_results <- fit_resamples(spec_bagged,
                            ___, 
                            resamples = vfold_cv(___),
                            metrics = ___)

# Collect metrics
___(cv_results)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Machine Learning met boomgebaseerde modellen in R

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4.9+

Begin de cursus gratis

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: Continuous outcomes Exercise 2: Train a regression tree Exercise 3: Predict new values Exercise 4: Inspect model output Exercise 5: Performance metrics for regression trees Exercise 6: In-sample performance Exercise 7: Out-of-sample performance Exercise 8: Bigger mistakes, bigger penalty Exercise 9: Cross-validation Exercise 10: Create the folds Exercise 11: Fit the folds Exercise 12: Evaluate the folds Exercise 13: Bias-variance tradeoff Exercise 14: Call things by their names Exercise 15: Adjust model complexity Exercise 16: In-sample and out-of-sample performance

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: Hyperparameters afstemmen Exercise 2: Genereer een tuningraster Exercise 3: Afstemmen langs het raster Exercise 4: Kies de winnaar Exercise 5: Meer modelmaten Exercise 6: Specificity berekenen Exercise 7: Teken de ROC-curve Exercise 8: Oppervlakte onder de ROC-curve Exercise 9: Gebagde bomen Exercise 10: Bagged trees maken Exercise 11: In-sample ROC en AUC Exercise 12: Controleer op overfitting

Huidige oefening

Exercise 13: Random forest Exercise 14: Bagged trees vs. random forest Exercise 15: Belang van variabelen

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