scikit-learn'in KFold() yöntemi

Az önce bir meslektaşının, rastgele orman modeli kurup örneklem dışı doğruluğu hesaplayan kodunu çalıştırdın. Meslektaşının kodunda random_state olmadığını ve bulduğun hataların onun rapor ettiklerinden tamamen farklı olduğunu fark ettin.

Bu rastgele orman modelinin yeni veride ne kadar doğru olacağına dair daha iyi bir tahmin elde etmek için KFold çapraz doğrulama için kullanılacak bazı indeksler üretmeye karar verdin.

Bu egzersiz, kursun bir parçasıdır

Python'da Model Doğrulama

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Egzersiz talimatları

Veriyi beş kat, karıştırma etkin, random_state=1111 olacak şekilde bölmek için KFold() metodunu çağır.
KFold nesnesinin split() metodunu X üzerinde kullan.
Eğitim ve doğrulama indeks listelerindeki indeks sayısını yazdır.

Uygulamalı etkileşimli egzersiz

Bu egzersizi bu örnek kodu tamamlayarak deneyin.

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(____))

Kodu Düzenle ve Çalıştır

Bu egzersiz, kursun bir parçasıdır

Python'da Model Doğrulama

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4.9+

Kursa Ücretsiz Başla

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: Holdout kümeleriyle ilgili sorunlar Exercise 2: İki örneklem Exercise 3: Olası sorunlar Exercise 4: Çapraz doğrulama Exercise 5: scikit-learn'in KFold() yöntemi

Geçerli egzersiz

Exercise 6: KFold indekslerini kullanma Exercise 7: sklearn'in cross_val_score() işlevi Exercise 8: scikit-learn yöntemleri Exercise 9: cross_val_score() uygulaması Exercise 10: Leave-one-out cross-validation (LOOCV)Exercise 11: LOOCV ne zaman kullanılmalı 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!