Penyetelan sepanjang grid

Setelah membuat grid penyetelan dan spesifikasi dummy, Anda perlu melatih model pada setiap titik grid dan mengevaluasi model yang dihasilkan. Ini sangat mudah dilakukan dalam kerangka kerja tidymodels menggunakan fungsi tune_grid(), seperti diperkenalkan di slide.

Pada latihan berikutnya, Anda akan menggunakan himpunan data pelanggan kartu kredit, yang memiliki kolom-kolom berikut:

still_customer: penanda (yes atau no) yang menunjukkan apakah seorang pelanggan masih aktif
total_trans_amt: total jumlah transaksi dalam USD
customer_age: usia pelanggan
income_category: label seperti $60K - $80K atau Less than $40K untuk menunjukkan kategori pendapatan tahunan
… dan 16 kolom lainnya.

Silakan periksa tibble customers di konsol! Hasil dari latihan sebelumnya, tree_grid dan tune_spec, masih dimuat.

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di R

Petunjuk latihan

Buat tiga lipatan cross-validation dari himpunan data Anda dan simpan sebagai folds.
Buat tune_results dengan menyetel spesifikasi sepanjang grid menggunakan semua prediktor untuk memprediksi still_customer, lipatan CV Anda sebagai resamples, dan metric_set(accuracy).
Gunakan autoplot() untuk membuat plot hasil penyetelan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

set.seed(275)

# Create CV folds of the customers tibble
folds <- ___

# Tune along the grid
tune_results <- tune_grid(___, 
                          ___,
                          resamples = ___,
                          grid = ___,
                          metrics = ___)

# Plot the tuning results
___(tune_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

Latihan Saat Ini

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 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