Latih tiap lipatan

Sekarang setelah Anda membagi data menjadi lipatan, saatnya menggunakannya untuk melatih model dan menghitung galat out-of-sample untuk setiap model. Dengan cara ini, Anda memperoleh estimasi yang seimbang atas kinerja spesifikasi model Anda karena Anda mengevaluasinya beberapa kali di luar sampel.

Di ruang kerja Anda tersedia chocolate_folds, yang Anda buat pada latihan sebelumnya (10 lipatan dari himpunan data pelatihan cokelat).

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di R

Petunjuk latihan

Tunjukkan bahwa Anda masih bisa: buat tree_spec, spesifikasi pohon regresi menggunakan engine "rpart".
Gunakan fit_resamples() untuk menyesuaikan lipatan Anda ke tree_spec, memodelkan final_grade menggunakan semua prediktor lain dan mengevaluasi dengan MAE dan RMSE.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create a specification
tree_spec <- ___ %>%
    ___
    ___

# Fit all folds to the specification
fits_cv <- ___(tree_spec,
               ___,
               resamples = ___,
               metrics = ___)

fits_cv

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 Exercise 8: Kesalahan lebih besar, penalti lebih besar Exercise 9: Cross-validation Exercise 10: Buat lipatan Exercise 11: Latih tiap lipatan

Latihan Saat Ini

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