GB regresörünü değerlendir

Artık test kümesi tahminleri elinde olduğuna göre, bunları gb'nin test kümesi Kök Ortalama Kare Hatası (RMSE) değerini hesaplamak için kullanabilirsin.

y_test ve y_pred tahminleri çalışma alanında mevcut.

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.
Test kümesi MSE değerini hesapla ve mse_test değişkenine ata.
Test kümesi RMSE değerini hesapla ve rmse_test değişkenine ata.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import mean_squared_error as MSE
____

# Compute MSE
mse_test = ____

# Compute RMSE
rmse_test = ____

# Print RMSE
print('Test set RMSE of gb: {:.3f}'.format(rmse_test))

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

Classification and Regression Trees (CART) are a set of supervised learning models used for problems involving classification and regression. In this chapter, you'll be introduced to the CART algorithm.

Exercise 1: Decision tree for classification Exercise 2: Train your first classification tree Exercise 3: Evaluate the classification tree Exercise 4: Logistic regression vs classification tree Exercise 5: Classification tree Learning Exercise 6: Growing a classification tree Exercise 7: Using entropy as a criterion Exercise 8: Entropy vs Gini index Exercise 9: Decision tree for regression Exercise 10: Train your first regression tree Exercise 11: Evaluate the regression tree Exercise 12: Linear regression vs regression tree

The bias-variance tradeoff is one of the fundamental concepts in supervised machine learning. In this chapter, you'll understand how to diagnose the problems of overfitting and underfitting. You'll also be introduced to the concept of ensembling where the predictions of several models are aggregated to produce predictions that are more robust.

Exercise 1: Generalization Error Exercise 2: Complexity, bias and variance Exercise 3: Overfitting and underfitting Exercise 4: Diagnose bias and variance problems Exercise 5: Instantiate the model Exercise 6: Evaluate the 10-fold CV error Exercise 7: Evaluate the training error Exercise 8: High bias or high variance?Exercise 9: Ensemble Learning Exercise 10: Define the ensemble Exercise 11: Evaluate individual classifiers Exercise 12: Better performance with a Voting Classifier

Bagging is an ensemble method involving training the same algorithm many times using different subsets sampled from the training data. In this chapter, you'll understand how bagging can be used to create a tree ensemble. You'll also learn how the random forests algorithm can lead to further ensemble diversity through randomization at the level of each split in the trees forming the ensemble.

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

Boosting refers to an ensemble method in which several models are trained sequentially with each model learning from the errors of its predecessors. In this chapter, you'll be introduced to the two boosting methods of AdaBoost and Gradient Boosting.

Exercise 1: Adaboost Exercise 2: AdaBoost sınıflandırıcısını tanımla Exercise 3: AdaBoost sınıflandırıcısını eğit Exercise 4: AdaBoost sınıflandırıcısını değerlendir Exercise 5: Gradient Boosting (GB)Exercise 6: GB regresörünü tanımla Exercise 7: GB regresörünü eğit Exercise 8: GB regresörünü değerlendir

Geçerli Egzersiz

Exercise 9: Stokastik Gradient Boosting (SGB)Exercise 10: SGB ile regresyon Exercise 11: SGB regresörünü eğit Exercise 12: SGB regresörünü değerlendir

The hyperparameters of a machine learning model are parameters that are not learned from data. They should be set prior to fitting the model to the training set. In this chapter, you'll learn how to tune the hyperparameters of a tree-based model using grid search cross validation.

Exercise 1: Tuning a CART's Hyperparameters Exercise 2: Tree hyperparameters Exercise 3: Set the tree's hyperparameter grid Exercise 4: Search for the optimal tree Exercise 5: Evaluate the optimal tree Exercise 6: Tuning a RF's Hyperparameters Exercise 7: Random forests hyperparameters Exercise 8: Set the hyperparameter grid of RF Exercise 9: Search for the optimal forest Exercise 10: Evaluate the optimal forest Exercise 11: Congratulations!