Topluluğu (ensemble) tanımla

Bu egzersiz setinde, UCI Machine Learning deposundaki Indian Liver Patient Dataset ile çalışacaksın.

Bu egzersizde, veri kümesindeki tüm özellikleri kullanarak bir hastada karaciğer hastalığı olup olmadığını tahmin etmek için üç sınıflandırıcıyı başlatacaksın.

KNN takma adıyla içeri aktarılan LogisticRegression, DecisionTreeClassifier ve KNeighborsClassifier sınıfları çalışma alanında hazır.

Bu egzersiz

Python ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Bir Lojistik Regresyon sınıflandırıcısı başlat ve lr değişkenine ata.
27 en yakın komşuyu dikkate alan bir KNN sınıflandırıcısı başlat ve knn değişkenine ata.
min_samples_leaf parametresi 0.13 olacak şekilde bir Karar Ağacı Sınıflandırıcısı başlat ve dt değişkenine ata.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Set seed for reproducibility
SEED=1

# Instantiate lr
lr = ____(random_state=SEED)

# Instantiate knn
knn = ____(n_neighbors=____)

# Instantiate dt
dt = ____(min_samples_leaf=____, random_state=SEED)

# Define the list classifiers
classifiers = [('Logistic Regression', lr), ('K Nearest Neighbours', knn), ('Classification Tree', dt)]

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: 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?Exercise 9: Ensemble Öğrenme Exercise 10: Topluluğu (ensemble) tanımla

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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

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!