Buat dua himpunan holdout

Baru-baru ini Anda membuat model random forest sederhana untuk memprediksi kemenangan permainan Tic-Tac-Toe untuk atasan Anda, dan atas permintaannya, Anda tidak melakukan penalaan parameter. Sayangnya, akurasi keseluruhan model terlalu rendah untuk standarnya. Kali ini, beliau meminta Anda berfokus pada kinerja model.

Sebelum mulai menguji berbagai model dan set parameter, Anda perlu membagi data menjadi himpunan data pelatihan, validasi, dan pengujian. Ingat bahwa setelah membagi data menjadi himpunan pelatihan dan pengujian, himpunan validasi dibuat dengan membagi himpunan pelatihan.

Himpunan data X dan y telah dimuat untuk Anda gunakan.

Latihan ini adalah bagian dari kursus

Validasi Model di Python

Petunjuk latihan

Buat himpunan data sementara dan himpunan data pengujian (X_test, y_test). Gunakan 20% dari keseluruhan data untuk himpunan data pengujian.
Dengan menggunakan himpunan data sementara (X_temp, y_temp), buat himpunan data pelatihan (X_train, y_train) dan validasi (X_val, y_val).
Gunakan 25% dari data sementara untuk himpunan data validasi.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create temporary training and final testing datasets
X_temp, ____, y_temp, ____  =\
    train_test_split(X, y, ____=____, random_state=1111)

# Create the final training and validation datasets
____, ____, ____, ____ =\
    train_test_split(X_temp, y_temp, ____=____, random_state=1111)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Validasi Model di Python

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

Before we can validate models, we need an understanding of how to create and work with them. This chapter provides an introduction to running regression and classification models in scikit-learn. We will use this model building foundation throughout the remaining chapters.

Exercise 1: Introduction to model validation Exercise 2: Modeling steps Exercise 3: Seen vs. unseen data Exercise 4: Regression models Exercise 5: Set parameters and fit a model Exercise 6: Feature importances Exercise 7: Classification models Exercise 8: Classification predictions Exercise 9: Reusing model parameters Exercise 10: Random forest classifier

This chapter focuses on the basics of model validation. From splitting data into training, validation, and testing datasets, to creating an understanding of the bias-variance tradeoff, we build the foundation for the techniques of K-Fold and Leave-One-Out validation practiced in chapter three.

Exercise 1: Membuat himpunan data train, test, dan validation Exercise 2: Buat satu holdout set Exercise 3: Buat dua himpunan holdout

Latihan Saat Ini

Exercise 4: Mengapa menggunakan holdout set Exercise 5: Metrik akurasi: model regresi Exercise 6: Mean absolute error Exercise 7: Mean squared error Exercise 8: Kinerja pada subset data Exercise 9: Metrik klasifikasi Exercise 10: Confusion matrix Exercise 11: Matriks kebingungan, lagi Exercise 12: Precision vs. recall Exercise 13: Pertukaran bias-varians Exercise 14: Galat akibat under/overfitting Exercise 15: Apakah saya underfitting?

Holdout sets are a great start to model validation. However, using a single train and test set if often not enough. Cross-validation is considered the gold standard when it comes to validating model performance and is almost always used when tuning model hyper-parameters. This chapter focuses on performing cross-validation to validate model performance.

Exercise 1: The problems with holdout sets Exercise 2: Two samples Exercise 3: Potential problems Exercise 4: Cross-validation Exercise 5: scikit-learn's KFold()Exercise 6: Using KFold indices Exercise 7: sklearn's cross_val_score()Exercise 8: scikit-learn's methods Exercise 9: Implement cross_val_score()Exercise 10: Leave-one-out-cross-validation (LOOCV)Exercise 11: When to use LOOCV Exercise 12: Leave-one-out-cross-validation

The first three chapters focused on model validation techniques. In chapter 4 we apply these techniques, specifically cross-validation, while learning about hyperparameter tuning. After all, model validation makes tuning possible and helps us select the overall best model.

Exercise 1: Introduction to hyperparameter tuning Exercise 2: Creating Hyperparameters Exercise 3: Running a model using ranges Exercise 4: RandomizedSearchCV Exercise 5: Preparing for RandomizedSearch Exercise 6: Implementing RandomizedSearchCV Exercise 7: Selecting your final model Exercise 8: Best classification accuracy Exercise 9: Selecting the best precision model Exercise 10: Course completed!