Conteúdo do Curso

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

Tarefa

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.}

Tarefa

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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Tudo estava claro?

Seção 2. Capítulo 2

Challenge: Solving Task Using Bagging Classifier

Tarefa

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.}

Tarefa

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.}

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Seção 2. Capítulo 2

Challenge: Solving Task Using Bagging Classifier

Tarefa

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.}

Tarefa

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.}

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Tarefa

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.}

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Seção 2. Capítulo 2

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