Luas di bawah kurva ROC

Luas di bawah kurva ROC merangkum banyak estimasi kinerja lainnya menjadi satu angka tunggal dan memungkinkan Anda menilai kinerja model dengan sangat cepat. Karena alasan ini, ukuran kinerja ini sangat umum digunakan untuk model klasifikasi.

Dengan AUC, Anda dapat menilai kinerja model menggunakan sistem penilaian huruf, di mana A adalah nilai terbaik:

AUC	Nilai
0,9 - 1	A
0,8 - 0,9	B
0,7 - 0,8	C
0,6 - 0,7	D
0,5 - 0,6	E

Anda akan menghitung AUC model Anda menggunakan tibble predictions dari latihan sebelumnya, yang masih dimuat.

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di R

Petunjuk latihan

Hitung luas di bawah kurva ROC menggunakan fungsi roc_auc() dan tibble predictions.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Calculate area under the curve
auc_result <- ___(___, 
                  estimate = ___, 
                  truth = ___)

print(paste("The area under the ROC curve is", round(auc_result$.estimate, 3)))

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

Latihan Saat Ini

Exercise 9: Pohon hasil bagging Exercise 10: Membuat bagged trees Exercise 11: ROC dan AUC dalam-sampel Exercise 12: Periksa overfitting 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