Kinerja out-of-sample

Kinerja in-sample memberikan wawasan tentang seberapa baik sebuah model menangkap data yang dimodelkannya. Untuk model prediktif, penting juga memeriksa kinerja model pada data baru yang belum pernah dilihat, yaitu kinerja out-of-sample.

Pada latihan ini, Anda akan memeriksa prediksi himpunan uji dari model Anda menggunakan MAE (mean absolute error/galat absolut rata-rata).

Telah dimuat kembali di ruang kerja Anda model yang Anda bangun dan gunakan pada latihan sebelumnya.

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di R

Petunjuk latihan

Gunakan model untuk memprediksi final_grade out-of-sample dan tambahkan prediksi Anda ke chocolate_test menggunakan bind_cols().
Hitung mean absolute error menggunakan fungsi dari yardstick.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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
___(___,
    ___,
    ___)

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: Keluaran kontinu Exercise 2: Latih pohon regresi Exercise 3: Memprediksi nilai baru Exercise 4: Periksa keluaran model Exercise 5: Metrik kinerja untuk pohon regresi Exercise 6: Kinerja in-sample Exercise 7: Kinerja out-of-sample

Latihan Saat Ini

Exercise 8: Kesalahan lebih besar, penalti lebih besar Exercise 9: Cross-validation Exercise 10: Buat lipatan Exercise 11: Latih tiap lipatan Exercise 12: Evaluasi lipatan Exercise 13: Pertukaran bias-varian Exercise 14: Sebutkan sesuai istilahnya Exercise 15: Sesuaikan kompleksitas model Exercise 16: Kinerja in-sample dan out-of-sample

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