Menghindari ketidakseimbangan kelas

Beberapa data memiliki keluaran yang sangat tidak seimbang—misalnya himpunan data penyakit langka. Jika membagi secara acak, Anda bisa mendapatkan pembagian yang sangat merugikan. Bayangkan semua observasi langka masuk ke set uji dan tidak ada di set latih. Itu akan merusak seluruh proses pelatihan Anda!

Untungnya, fungsi initial_split() menyediakan solusinya. Pada latihan ini, Anda akan mengamati dan mengatasi apa yang disebut sebagai ketidakseimbangan kelas.

Kode telah disediakan untuk membuat objek split diabetes_split dengan 75% data latih dan 25% data uji.

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Preparation
set.seed(9888)
diabetes_split <- initial_split(diabetes, prop = 0.75)

# Proportion of 'yes' outcomes in the training data
counts_train <- table(training(___)$outcome)
prop_yes_train <- counts_train["___"] / sum(counts_train)

# Proportion of 'yes' outcomes in the test data
counts_test <- table(___)
prop_yes_test <- ___ / sum(___)

paste("Proportion of positive outcomes in training set:", round(prop_yes_train, 2))
paste("Proportion of positive outcomes in test set:", round(prop_yes_test, 2))

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: Selamat datang di kursus ini!Exercise 2: Mengapa metode berbasis pohon?Exercise 3: Tentukan spesifikasi pohon tersebut Exercise 4: Latih model tersebut Exercise 5: Cara menumbuhkan pohon Anda Exercise 6: Pemecahan train/test Exercise 7: Menghindari ketidakseimbangan kelas

Latihan Saat Ini

Exercise 8: Dari nol jadi andal Exercise 9: Prediksi dan evaluasi Exercise 10: Buat prediksi Exercise 11: Uraikan matriks Exercise 12: Apakah Anda memprediksi dengan benar?

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: 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