Penyetelan yang sebenarnya

Hiperparameter terbaik menghasilkan model terbaik untuk data Anda. Setelah Anda menentukan grid penyetelan, Anda perlu melatih dan mengevaluasi model pada setiap titik grid untuk melihat titik grid mana yang memberikan kinerja model terbaik.

Ini dapat memakan waktu, mengingat dengan menggunakan validasi silang k-lipat, ukuran ensemble n pohon, dan grid penyetelan dengan t kombinasi menghasilkan total k * n * t model yang harus dilatih.

Sekarang giliran Anda melakukan penyetelan yang sebenarnya! Telah dimuat sebelumnya customers_train serta hasil dari latihan terakhir, boost_spec dan tunegrid_boost:

# A tibble: 27 x 3
   tree_depth    learn_rate  sample_size
        <int>         <dbl>        <dbl>
 1          1  0.0000000001         0.1 
 2          8  0.0000000001         0.1 
 3         15  0.0000000001         0.1 
 4          1  0.00000316           0.1 
 ...

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di R

Petunjuk latihan

Buat enam lipatan dari data pelatihan menggunakan vfold_cv() dan simpan sebagai folds.
Gunakan tune_grid() untuk menyetel boost_spec dengan lipatan Anda, grid penyetelan Anda, dan metrik roc_auc. Simpan hasilnya sebagai tune_results.
Visualisasikan hasilnya dengan membuat plot proses penyetelan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create CV folds of training data
folds <- ___

# Tune along the grid
tune_results <- ___(___,
                    still_customer ~ .,
                    resamples = ___,
                    grid = ___,
                    metrics = metric_set(___))

# Plot the 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: 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: Pengantar boosting Exercise 2: Bagging vs. boosting Exercise 3: Menentukan ensemble boosted Exercise 4: Gradient boosting Exercise 5: Latih sebuah boosted ensemble Exercise 6: Evaluasi ansambel Exercise 7: Bandingkan dengan satu pengklasifikasi Exercise 8: Optimalkan ansambel boosting Exercise 9: Persiapan penyetelan Exercise 10: Penyetelan yang sebenarnya

Latihan Saat Ini

Exercise 11: Finalisasi model Exercise 12: Perbandingan model Exercise 13: Bandingkan AUC Exercise 14: Plot kurva ROC Exercise 15: Penutup