Örnek içi ROC ve AUC

Bagging uygulanmış ağaçlar, eğitim kümeninin yapısını ne kadar iyi yakalıyor? Karar ağaçlarından daha mı iyi? Aşırı öğreniyorlar mı? Bunu değerlendirmek için ROC ve AUC harika araçlardır.

Bu egzersizde, bu örnek içi tahminleri üretecek ve ROC ile AUC değerlerini hesaplayacaksın. Dikkat, sürprizler var!

Önceden yüklenenler: önceki egzersizin çıktısı model_bagged ve eğitim verisi customers_train.

Bu egzersiz

R ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Eğitim kümenle olasılık tahminleri üretmek için model_bagged modelini kullan, bu tahminleri eğitim tibble'ına ekle ve sonucu predictions olarak kaydet.
predictions tibble'ının ROC eğrisini oluştur ve sonucu görselleştir.
predictions tibble'ının AUC değerini hesapla.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Predict on training set and add to training set
predictions <- ___(___,
                   new_data = ___, 
                   type = "___") %>% 
    bind_cols(___)

# Create and plot the ROC curve
roc_curve(___,
          estimate = ___,
          truth = ___) %>% autoplot()

# Calculate the AUC
___(predictions,
    estimate = ___, 
    truth = ___)

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

InicianteNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

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: Hiperparametreleri ayarlama Exercise 2: Bir ayar ızgarası oluştur Exercise 3: Izgara boyunca ayarla Exercise 4: Kazananı seç Exercise 5: Daha fazla model ölçütü Exercise 6: Özgüllüğü (specificity) hesapla Exercise 7: ROC eğrisini çiz Exercise 8: ROC eğrisi altında kalan alan Exercise 9: Torbalanmış ağaçlar Exercise 10: Bagging ağaçları oluştur Exercise 11: Örnek içi ROC ve AUC

Geçerli Egzersiz

Exercise 12: Aşırı uyumu kontrol et Exercise 13: Rastgele orman Exercise 14: Çantalanmış ağaçlar ve rastgele orman Exercise 15: Değişken önemi

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