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

Ensemble Learning

ExtraTrees

Extra Trees, short for Extremely Randomized Trees, is a bagging ensemble learning technique that builds upon the concept of decision trees to create a more robust and diverse model.

How does ExtraTrees algorithm work?

It is a variation of the Random Forest algorithm but introduces even more randomness into the tree-building process:

The extra trees algorithm, like the random forests algorithm, creates many decision trees, but the sampling for each tree is random, without replacement;
A specific number of features from the total set of features is also selected randomly for each tree;
Extra trees' most important and unique characteristic is the random selection of a splitting value for a feature. Instead of calculating a locally optimal value using Gini or entropy to split the data, the algorithm randomly selects a split value. This makes the trees diversified and uncorrelated.
Note
We can also use .feature_importances_ attribute to measure the features' impact on the model's result.

Example

We can use ExtraTrees in Python just like Random Forest using the ExtraTreesClassifier or ExtraTreesRegressor classes:

You can find the official documentation with all the necessary information about implementing this model in Python on the official website. Go here if needed.

Tudo estava claro?

Seção 2. Capítulo 7

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

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

Ensemble Learning

ExtraTrees

Extra Trees, short for Extremely Randomized Trees, is a bagging ensemble learning technique that builds upon the concept of decision trees to create a more robust and diverse model.

How does ExtraTrees algorithm work?

It is a variation of the Random Forest algorithm but introduces even more randomness into the tree-building process:

The extra trees algorithm, like the random forests algorithm, creates many decision trees, but the sampling for each tree is random, without replacement;
A specific number of features from the total set of features is also selected randomly for each tree;
Extra trees' most important and unique characteristic is the random selection of a splitting value for a feature. Instead of calculating a locally optimal value using Gini or entropy to split the data, the algorithm randomly selects a split value. This makes the trees diversified and uncorrelated.
Note
We can also use .feature_importances_ attribute to measure the features' impact on the model's result.

Example

We can use ExtraTrees in Python just like Random Forest using the ExtraTreesClassifier or ExtraTreesRegressor classes:

You can find the official documentation with all the necessary information about implementing this model in Python on the official website. Go here if needed.

Tudo estava claro?

Seção 2. Capítulo 7