Kursinnehåll

Ensemble Learning

1. Basic Principles of Building Ensemble Models

What is Ensemble of Models?Bagging Models Boosting Models Stacking Models

2. Commonly Used Bagging Models

Bagging Classifier Challenge: Solving Task Using Bagging Classifier Bagging Regressor Challenge: Solving Task Using Bagging Regressor Random Forest Challenge: Determining Feature Importances Using Random Forest ExtraTrees

3. Commonly Used Boosting Models

AdaBoost Classifier Challenge: Solving Task Using AdaBoost Classifier Challenge: Solving Task Using AdaBoost Regressor Gradient Boosting XGBoost Challenge: Solving Task Using XGBoost

4. Commonly Used Stacking Models

Stacking Classifier Challenge: Solving Task Using Stacking Classifier Challenge: Solving Task Using Stacking Regressor Using Ensembles As Base Models Course Summary

Challenge: Solving Task Using Bagging Classifier

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The load_breast_cancer dataset is a built-in dataset provided by scikit-learn. It is commonly used for binary classification tasks, particularly in the context of breast cancer diagnosis. This dataset contains features that are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The aim is to predict whether a given mass is malignant (cancerous) or benign (non-cancerous).

Your task is to solve the classification problem using BaggingClassifier on load_breast_cancer dataset:

Create an instance of BaggingClassifier class: specify base SVC (Support Vector Classifier) model and set the number of base estimators equal to 10.
Fit the ensemble model.
Get the final result using soft voting technique: for each sample in test dataset get the probability matrix and find the class with maximum probability.

Lösning

Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 2. Kapitel 2

Challenge: Solving Task Using Bagging Classifier

Uppgift

Swipe to start coding

Your task is to solve the classification problem using BaggingClassifier on load_breast_cancer dataset:

Create an instance of BaggingClassifier class: specify base SVC (Support Vector Classifier) model and set the number of base estimators equal to 10.
Fit the ensemble model.
Get the final result using soft voting technique: for each sample in test dataset get the probability matrix and find the class with maximum probability.

Lösning

Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 2. Kapitel 2

Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan