Leave-one-out-cross-validation

Diyelim ki en sevdiğin şekerleme candy veri kümesinde yok ve sen de bu şekerlemenin popülerliğiyle ilgileniyorsun. 5 katlı çapraz doğrulama kullanmak, her seferinde verinin sadece %80'i üzerinde eğitim yapar. Ancak candy veri kümesinde sadece 85 satır var ve verinin %20'sini dışarıda bırakmak modelimize zarar verebilir. Buna karşılık, leave-one-out-cross-validation (LOOCV) sınırlı veri kümesini en iyi şekilde kullanmamıza olanak tanır ve en sevdiğin şekerlemenin popülerliği için en iyi tahmini verir!

Bu egzersizde, LOOCV gerçekleştirmek için cross_val_score() kullanacaksın.

Bu egzersiz

Python'da Model Doğrulama

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

cross_val_score() için mean_absolute_error kullanarak bir skorlayıcı oluştur.
cross_val_score() fonksiyonunu; model olarak rfr, yeni tanımlanan mae_scorer ve LOOCV kullanılacak şekilde doldur.
numpy (kısaltılmış adıyla np) kullanarak scores'un ortalamasını ve standart sapmasını yazdır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

from sklearn.metrics import mean_absolute_error, make_scorer

# Create scorer
mae_scorer = ____(____)

rfr = RandomForestRegressor(n_estimators=15, random_state=1111)

# Implement LOOCV
scores = cross_val_score(____, X=X, y=y, cv=____, scoring=____)

# Print the mean and standard deviation
print("The mean of the errors is: %s." % np.____(____))
print("The standard deviation of the errors is: %s." % np.____(____))

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python'da Model Doğrulama

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

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 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

Geçerli Egzersiz

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!