Hitung specificity

Menggunakan metrik yang berbeda untuk kinerja model memungkinkan Anda menilainya dengan lebih akurat. Ada beberapa metrik untuk berbagai kasus penggunaan. Specificity mengukur proporsi true negative yang teridentifikasi dengan benar:

$$\text{specificity} = \frac{TN}{TN + FP}$$

Rumus ini menyiratkan bahwa ketika specificity mendekati 100%, jumlah false positive (FP) mendekati 0.

Dalam latihan ini, Anda akan menyelidiki specificity out-of-sample dari model Anda dengan cross-validation.

Telah dimuat sebelumnya data pelatihan dari himpunan data pelanggan kartu kredit, customers_train, dan spesifikasi pohon keputusan, tree_spec, yang dihasilkan menggunakan kode berikut:

tree_spec <- decision_tree() %>% 
                set_engine("rpart") %>%
                set_mode("classification")

Latihan ini adalah bagian dari kursus

Machine Learning dengan Model Berbasis Pohon di R

Petunjuk latihan

Buat tiga lipatan CV dari customers_train dan simpan sebagai folds.
Hitung specificity tervalidasi-silang menggunakan fungsi fit_resamples() yang menerima spesifikasi tree_spec, formula model, lipatan CV, dan himpunan metrik yang sesuai. Gunakan semua prediktor untuk memprediksi still_customer, lalu simpan hasilnya ke specificities.
Agregasikan hasil menggunakan satu fungsi.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create CV folds of the training data
folds <- ___(customers_train, v = ___)

# Calculate CV specificity
specificities <- ___(___, 
                     ___,
                     resamples = ___,
                     metrics = ___)

# Collect the metrics
___(specificities)

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 Exercise 4: Pilih pemenang Exercise 5: Ukuran model lainnya Exercise 6: Hitung specificity

Latihan Saat Ini

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