Karmaşıklık matrisleri

Karmaşıklık matrisleri, modelinin doğruluğunu keşfetmeye başlamak için harika bir yoldur. Duyarlılık (sensitivity), özgüllük (specificity) ve F1-skora kadar geniş bir dizi metriği hesaplamak için gereken değerleri sağlar.

Bir kişinin röntgen görüntüsüne bakarak kolunun kırık olup olmadığını tahmin eden bir sınıflandırma modeli kurdun. Test kümesinde aşağıdaki karmaşıklık matrisi var:

	Tahmin: 0	Tahmin: 1
Gerçek: 0	324 (TN)	15 (FP)
Gerçek: 1	123 (FN)	491 (TP)

Bu egzersiz

Python'da Model Doğrulama

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Genel doğruluğu (accuracy) hesaplamak için karmaşıklık matrisini kullan.
Precision ve recall değerlerini hesaplamak için karmaşıklık matrisini kullan.
Üç print ifadesini kullanarak her bir doğruluk değerini yazdır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Calculate and print the accuracy
accuracy = (____ + ____) / (953)
print("The overall accuracy is {0: 0.2f}".format(accuracy))

# Calculate and print the precision
precision = (____) / (____ + ____)
print("The precision is {0: 0.2f}".format(precision))

# Calculate and print the recall
recall = (____) / (____ + ____)
print("The recall is {0: 0.2f}".format(recall))

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: Eğitim, test ve doğrulama veri kümeleri oluşturma Exercise 2: Tek bir holdout kümesi oluştur Exercise 3: İki holdout kümesi oluştur Exercise 4: Neden ayırma (holdout) kümeleri kullanılır Exercise 5: Doğruluk ölçütleri: regresyon modelleri Exercise 6: Ortalama mutlak hata Exercise 7: Ortalama kare hata Exercise 8: Veri alt kümelerinde performans Exercise 9: Sınıflandırma metrikleri Exercise 10: Karmaşıklık matrisleri

Geçerli Egzersiz

Exercise 11: Karışıklık matrisleri, yeniden Exercise 12: Precision ve recall Exercise 13: Bias-variance dengesi Exercise 14: Az/Aşırı uyumdan kaynaklanan hata Exercise 15: Aşırı basitleştiriyor muyum (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!