Sınıflandırma ağacını değerlendir

İlk sınıflandırma ağacını eğittiğine göre, şimdi de test kümesi üzerindeki performansını değerlendirme zamanı. Bunu, test kümesi üzerinde yapılan doğru tahminlerin oranına karşılık gelen doğruluk (accuracy) metriğini kullanarak yapacaksın.

Önceki egzersizde eğitilmiş dt modeli, test kümesi özellik matrisi X_test ve etiket dizisi y_test çalışma alanına yüklendi.

Bu egzersiz

Python ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

sklearn.metrics içinden accuracy_score fonksiyonunu içe aktar.
Test kümesi etiketlerini tahmin et ve elde edilen diziyi y_pred değişkenine ata.
accuracy_score() çağırarak dt modelinin test kümesi doğruluk skorunu değerlendir ve değeri acc değişkenine ata.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import accuracy_score
from ____.____ import ____

# Predict test set labels
y_pred = ____.____(____)

# Compute test set accuracy  
acc = ____(____, ____)
print("Test set accuracy: {:.2f}".format(acc))

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: Sınıflandırma için karar ağacı Exercise 2: İlk sınıflandırma ağacını eğit Exercise 3: Sınıflandırma ağacını değerlendir

Geçerli Egzersiz

Exercise 4: Lojistik regresyon vs sınıflandırma ağacı Exercise 5: Sınıflandırma ağacı öğrenimi Exercise 6: Bir sınıflandırma ağacını büyütme Exercise 7: Ölçüt olarak entropi kullanma Exercise 8: Entropi vs Gini indeksi Exercise 9: Regresyon için karar ağacı Exercise 10: İlk regresyon ağacını eğit Exercise 11: Regresyon ağacını değerlendir Exercise 12: Doğrusal regresyon vs regresyon ağacı

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: 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!