Periksa overfitting

AUC dalam-sampel yang sangat tinggi seperti \(99.9\%\) dapat menjadi indikator overfitting. Ada juga kemungkinan bahwa himpunan data Anda memang sangat terstruktur dengan baik, atau model Anda benar-benar luar biasa!

Untuk memeriksa mana yang benar, Anda perlu menghasilkan estimasi AUC di luar sampel, dan karena Anda belum ingin menyentuh himpunan uji, Anda dapat menghitungnya menggunakan cross-validation pada himpunan latih.

Data latih Anda, customers_train, dan spesifikasi bagging tree, spec_bagged, masih tersedia di ruang kerja Anda.

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di R

Petunjuk latihan

Dengan fit_resamples(), estimasikan metrik roc_auc Anda menggunakan tiga lipatan CV pada himpunan latih dan rumus model still_customer ~ total_trans_amt + customer_age + education_level.
Kumpulkan metrik hasilnya untuk menampilkan AUC.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

set.seed(55)

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

# Collect metrics
___(cv_results)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di R

SkillTag.level.beginnerSkillTag.label

4.9+

Mulai Kursus 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: Menyetel hyperparameter Exercise 2: Hasilkan tuning grid Exercise 3: Penyetelan sepanjang grid Exercise 4: Pilih pemenang Exercise 5: Ukuran model lainnya Exercise 6: Hitung specificity Exercise 7: Gambar kurva ROC Exercise 8: Luas di bawah kurva ROC Exercise 9: Pohon hasil bagging Exercise 10: Membuat bagged trees Exercise 11: ROC dan AUC dalam-sampel Exercise 12: Periksa overfitting

Latihan Saat Ini

Exercise 13: Random forest Exercise 14: Bagged trees vs. random forest Exercise 15: Kepentingan variabel

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