En iyi ağacı değerlendir

Bu egzersizde, grid_dt'nin en iyi modelinin test seti ROC AUC skorunu değerlendireceksin.

Bunu yapmak için önce test setindeki her gözlem için pozitif etiketin elde edilme olasılığını belirleyeceksin. Bir sklearn sınıflandırıcısının predict_proba() metodunu kullanarak, sütunlar boyunca sırasıyla negatif ve pozitif sınıf etiketlerinin olasılıklarını içeren 2B bir dizi hesaplayabilirsin.

Veri kümesi senin için zaten yüklendi ve işlendi (sayısal özellikler standartlaştırıldı); veri %80 eğitim ve %20 test olarak bölündü. X_test, y_test çalışma alanında mevcut. Ayrıca, önceki egzersizde örneklediğin ve eğitilmiş GridSearchCV nesnesi grid_dt de yüklendi. grid_dt aşağıdaki gibi eğitildiğine dikkat et:

grid_dt.fit(X_train, y_train)

Bu egzersiz

Python ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

sklearn.metrics içinden roc_auc_score'u içe aktar.
grid_dt'den .best_estimator_ özniteliğini çıkar ve best_model değişkenine ata.
Test setinde pozitif sınıfı elde etme olasılıklarını y_pred_proba olarak tahmin et.
best_model için test seti ROC AUC skorunu test_roc_auc olarak hesapla.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import roc_auc_score from sklearn.metrics
____

# Extract the best estimator
best_model = ____

# Predict the test set probabilities of the positive class
y_pred_proba = ____

# Compute test_roc_auc
test_roc_auc = ____

# Print test_roc_auc
print('Test set ROC AUC score: {:.3f}'.format(test_roc_auc))

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

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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: Define the AdaBoost classifier Exercise 3: Train the AdaBoost classifier Exercise 4: Evaluate the AdaBoost classifier Exercise 5: Gradient Boosting (GB)Exercise 6: Define the GB regressor Exercise 7: Train the GB regressor Exercise 8: Evaluate the GB regressor Exercise 9: Stochastic Gradient Boosting (SGB)Exercise 10: Regression with SGB Exercise 11: Train the SGB regressor Exercise 12: Evaluate the SGB regressor

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: Bir CART'ın Hiperparametrelerini Ayarlama Exercise 2: Ağaç hiperparametreleri Exercise 3: Ağacın hiperparametre ızgarasını ayarla Exercise 4: En iyi ağacı ara Exercise 5: En iyi ağacı değerlendir

Geçerli Egzersiz

Exercise 6: Bir RF'in Hiperparametrelerini Ayarlama Exercise 7: Random Forests hiperparametreleri Exercise 8: RF'in hiperparametre ızgarasını ayarla Exercise 9: En iyi ormanı ara Exercise 10: En iyi ormanı değerlendir Exercise 11: Tebrikler!