Зміст курсу

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

Завдання

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.

_{Once you've completed this task, click the button below the code to check your solution.}

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Секція 2. Розділ 2

Challenge: Solving Task Using Bagging Classifier

Завдання

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.

_{Once you've completed this task, click the button below the code to check your solution.}

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 2. Розділ 2

Challenge: Solving Task Using Bagging Classifier

Завдання

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.

_{Once you've completed this task, click the button below the code to check your solution.}

Все було зрозуміло?

Дякуємо за ваш відгук!

Завдання

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.

_{Once you've completed this task, click the button below the code to check your solution.}

Секція 2. Розділ 2