Örnek dışı performans

Örnek içi performans, bir modelin modellediği veriyi ne kadar iyi yakaladığını gösterir. Öngörücü modeller için, modelin yeni ve görülmemiş verideki, yani örnek dışındaki performansını da kontrol etmek önemlidir.

Bu egzersizde, modelinin test kümesi tahminlerini MAE (ortalama mutlak hata) ile kontrol edeceksin.

Çalışma alanında, önceki egzersizlerde kurup kullandığın model tekrar yüklü olarak hazır.

Bu egzersiz

R ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Örnek dışı final_grade değerlerini tahmin etmek için modeli kullan ve tahminlerini bind_cols() ile chocolate_test veri kümesine ekle.
Ortalama mutlak hatayı bir yardstick fonksiyonu kullanarak hesapla.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Predict ratings on test set and add true grades
test_enriched <- predict(__, new_data = ___) %>%
    bind_cols(___)
    
# Compute the mean absolute error using one single function
___(___,
    ___,
    ___)

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: Sürekli çıktılar Exercise 2: Bir regresyon ağacı eğit Exercise 3: Yeni değerleri tahmin et Exercise 4: Model çıktısını incele Exercise 5: Regresyon ağaçları için performans ölçütleri Exercise 6: Örnek içi performans Exercise 7: Örnek dışı performans

Geçerli Egzersiz

Exercise 8: Büyük hatalar, büyük ceza Exercise 9: Çapraz doğrulama Exercise 10: Katmanları oluştur Exercise 11: Katları uydur Exercise 12: Katlamaları değerlendir Exercise 13: Sapma-varyans dengesi Exercise 14: Adlarıyla çağır Exercise 15: Model karmaşıklığını ayarla Exercise 16: Örnek içi ve örnek dışı performans

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