Ortalama kare hata

2017 NBA tahminlerine tekrar odaklanalım. Her yıl, beklenenden çok daha fazla maç kazanan en az birkaç NBA takımı olur. MAE kullanırsan, bu doğruluk metriği kötü tahminlerden kaynaklanan hataları MSE kullanmana kıyasla o kadar yansıtmaz. Büyük hataları kareye almak, doğruluğun daha kötü görünmesine neden olur.

Bu örnekte, NBA yöneticileri takım galibiyetlerini daha iyi tahmin etmek istiyor. Tahmin hatasını hesaplamak için ortalama kare hatayı kullanacaksın. Gerçek galibiyetler y_test olarak, tahminler ise predictions olarak yüklendi.

Bu egzersiz

Python'da Model Doğrulama

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

MSE'yi elle hesapla. $$ MSE = \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i ) ^2 }{n} $$
sklearn kullanarak MSE'yi hesapla.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

from sklearn.metrics import ____

n = ___(predictions)
# Finish the manual calculation of the MSE
mse_one = sum((y_test - predictions)____) / n
print('With a manual calculation, the error is {}'.format(mse_one))

# Use the scikit-learn function to calculate MSE
mse_two = ____
print('Using scikit-learn, the error is {}'.format(mse_two))

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

Geçerli Egzersiz

Exercise 8: Veri alt kümelerinde performans Exercise 9: Sınıflandırma metrikleri Exercise 10: Karmaşıklık matrisleri 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!