Course Content

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

Bagging Classifier

We have already considered the principle of the work of bagging ensemble. Now let's apply this knowledge and create a model that will provide classification using such an ensemble in Python:

Firstly, we import BaggingClassifier class that contains all the necessary tools to work with the bagging classifier.
Then, we create an instance of this class specifying the base model, the number of these models to create an ensemble, and n_jobs parameter.

Note
We have already mentioned that bagging models can be fitted using parallel computing. n_jobs=-1 means that we will use all available processors to train the model

Now we can use the .fit() and .predict() methods of BaggingClassifier to fit the model on available data and make predictions:

Now, let's talk about the base models of an ensemble.

What models can be used as base?

We can use absolutely any models designed to perform classification tasks as the base models of the ensemble (logistic regression, SVM, neural networks, etc.).

It is also important to note that when using the .fit() method, the ensemble will learn on different data subsamples by itself, so we don't need to specify additional parameters or manually control the learning process.

When we use the .predict() method, the hard voting technique creates a final prediction.
If we want to create a final prediction using soft voting, we must use the base estimator with the implemented .predict_proba() method instead of the .predict() method. As a result, we will get the vector corresponding to each sample from the test set containing aggregated probabilities belonging to a particular class (we will call it the probability matrix).

Note
If we don't specify the base model of BaggingClassifer, the Decision Tree Classifier will be used by default.

Example of usage

Let's solve some simple classification problems using a bagging ensemble with logistic regression as a base model:

Code Description

In the provided code, we performed the following steps:

Generate Synthetic Data:

We used the make_classification() function from sklearn.datasets module to create a synthetic dataset for a binary classification problem. The dataset has 1000 samples, 10 features, 5 informative features, and 1 cluster per class. This synthetic dataset allows us to create a simple classification problem for demonstration purposes.

Split Data into Training and Testing Sets:

We split the synthetic data into training and testing sets using train_test_split() function from sklearn.model_selection module. The training set will be used to train the Bagging ensemble, and the testing set will be used to evaluate its performance.

Create Base Model:

We created a base model using LogisticRegression class from sklearn.linear_model module. The base model is a Logistic Regression classifier, which will be used as the weak learner within the Bagging ensemble.

Create Bagging Classifier:

We created a BaggingClassifier class with the base model (Logistic Regression) as the weak learner. The BaggingClassifier will train multiple instances of the base model on different subsets of the training data.

Train the Bagging Classifier:

We trained the Bagging ensemble on the training data using the .fit() method. The BaggingClassifier sequentially trains multiple instances of the base model on different subsets of the training data to create the ensemble.

Make Predictions and Calculate F1 Score:

After training the Bagging ensemble, we used it to make predictions on the test data with the .predict() method. We then calculated the F1 score using f1_score() function from sklearn.metrics module.

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.

How can we use soft voting in `BaggingClassifier`?

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Section 2. Chapter 1

Ensemble Learning

Bagging Classifier

What models can be used as base?

Example of usage

How can we use soft voting in BaggingClassifier?

How can we use soft voting in `BaggingClassifier`?