KFold() di scikit-learn

Anda baru saja menjalankan kode rekan kerja yang membuat model random forest dan menghitung akurasi out-of-sample. Anda melihat bahwa kode rekan Anda tidak menyetel random state, dan kesalahan yang Anda temukan sama sekali berbeda dari yang dilaporkan rekan Anda.

Untuk mendapatkan perkiraan yang lebih baik tentang seberapa akurat model random forest ini pada data baru, Anda memutuskan untuk menghasilkan beberapa indeks untuk digunakan pada KFold cross-validation.

Latihan ini adalah bagian dari kursus

Validasi Model di Python

Petunjuk latihan

Panggil metode KFold() untuk membagi data menggunakan lima split, dengan pengacakan (shuffle), dan random state 1111.
Gunakan metode split() dari KFold pada X.
Cetak jumlah indeks pada daftar indeks train dan validation.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

from sklearn.model_selection import KFold

# Use KFold
kf = KFold(____, ____, ____)

# Create splits
splits = kf.____(____)

# Print the number of indices
for train_index, val_index in splits:
    print("Number of training indices: %s" % len(____))
    print("Number of validation indices: %s" % len(____))

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: Creating train, test, and validation datasets Exercise 2: Create one holdout set Exercise 3: Create two holdout sets Exercise 4: Why use holdout sets Exercise 5: Accuracy metrics: regression models Exercise 6: Mean absolute error Exercise 7: Mean squared error Exercise 8: Performance on data subsets Exercise 9: Classification metrics Exercise 10: Confusion matrices Exercise 11: Confusion matrices, again Exercise 12: Precision vs. recall Exercise 13: The bias-variance tradeoff Exercise 14: Error due to under/over-fitting Exercise 15: Am I 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: Masalah pada holdout set Exercise 2: Dua sampel Exercise 3: Potensi masalah Exercise 4: Cross-validation Exercise 5: KFold() di scikit-learn

Latihan Saat Ini

Exercise 6: Menggunakan indeks KFold Exercise 7: cross_val_score() dari sklearn Exercise 8: Metode-metode scikit-learn Exercise 9: Menerapkan cross_val_score()Exercise 10: Leave-one-out cross-validation (LOOCV)Exercise 11: Kapan menggunakan 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!