Yüksek bias mı, yüksek varyans mı?

Bu egzersizde, önceki egzersizde eğittiğin regresyon ağacı dt'nin bias mı yoksa varyans sorunu mu yaşadığını teşhis edeceksin.

dt tarafından elde edilen eğitim kümesi RMSE'si (RMSE_train) ve Çapraz Doğrulama RMSE'si (RMSE_CV) çalışma alanında mevcut. Ayrıca, yalnızca disp özelliğiyle eğitilen regresyon ağacının elde ettiği kök ortalama kare hata değerine karşılık gelen baseline_RMSE adlı bir değişkeni de yükledik (bu, bölüm 1, ders 3'te eğitilen regresyon ağacının RMSE'sidir). Burada baseline_RMSE, bir modelin bu değerin üzerinde kalması durumunda yetersiz öğrenme (underfitting) yaptığı, altında kalması durumunda ise "yeterince iyi" sayıldığı bir referans RMSE değeri olarak kullanılır.

dt yüksek bias mı yoksa yüksek varyans sorunu mu yaşıyor?

Bu egzersiz

Python ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

İnteraktif egzersizlerimizden biriyle teoriyi pratiğe dökün

Egzersizi başlat

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: 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 Exercise 8: Yüksek bias mı, yüksek varyans mı?

Geçerli Egzersiz

Exercise 9: Ensemble Öğrenme Exercise 10: Topluluğu (ensemble) tanımla Exercise 11: Tek tek sınıflandırıcıları değerlendir Exercise 12: Voting Classifier ile daha iyi performans

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!