Eğitim hatasını değerlendir

Şimdi, önceki bir egzersizde oluşturduğun regresyon ağacı dt'nin eğitim kümesi RMSE değerini değerlendireceksin.

dt'ye ek olarak, X_train ve y_train çalışma alanında hazır.

scikit-learn'de bir modelin MSE değerinin şu şekilde hesaplanabildiğini unutma:

MSE_model = mean_squared_error(y_true, y_predicted)

Burada metrics modülünden mean_squared_error fonksiyonunu kullanıyoruz ve ilk argüman olarak gerçek etiketleri y_true, ikinci argüman olarak ise modelin tahmin ettiği etiketleri y_predicted veriyoruz.

Bu egzersiz

Python ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

sklearn.metrics içinden mean_squared_error fonksiyonunu MSE takma adıyla içe aktar.
dt'yi eğitim kümesine fit et.
dt'nin eğitim kümesi etiketlerini tahmin et ve sonucu y_pred_train değişkenine ata.
dt'nin eğitim kümesi RMSE değerini hesapla ve RMSE_train değişkenine ata.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import mean_squared_error from sklearn.metrics as MSE
____

# Fit dt to the training set
____.____(____, ____)

# Predict the labels of the training set
____ = ____.____(____)

# Evaluate the training set RMSE of dt
____ = (____(____, ____))**(___)

# Print RMSE_train
print('Train RMSE: {:.2f}'.format(RMSE_train))

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

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Exercise 1: Genelleme Hatası Exercise 2: Karmaşıklık, önyargı ve varyans Exercise 3: Aşırı öğrenme (overfitting) ve yetersiz öğrenme (underfitting)Exercise 4: Bias ve varyans sorunlarını teşhis et Exercise 5: Modeli örnekle Exercise 6: 10 katlı ÇD hatasını değerlendir Exercise 7: Eğitim hatasını değerlendir

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Exercise 1: Bagging Exercise 2: Define the bagging classifier Exercise 3: Evaluate Bagging performance Exercise 4: Out of Bag Evaluation Exercise 5: Prepare the ground Exercise 6: OOB Score vs Test Set Score Exercise 7: Random Forests (RF)Exercise 8: Train an RF regressor Exercise 9: Evaluate the RF regressor Exercise 10: Visualizing features importances

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