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

Stacking Classifier

Stacking Classifier is a stacking ensemble model which is used to solve classification tasks. It aims to exploit the strengths of individual models by using their predictions as input for a higher-level model, known as the meta-classifier or second-level model. The meta-classifier learns how to combine the predictions from the base models to make the final classification decision.

How does Stacking Classifier work?

Base Models: Several different classification models are trained independently on the training data. These diverse models can utilize various algorithms, architectures, or parameter settings.
Prediction Generation: After training the base models, they are used to make predictions on both the training data. These predictions serve as features (meta-features) for the next level of modeling.
Meta-Classifier: A higher-level classifier (meta-classifier) is trained using the meta-features generated from the base models. The meta-classifier learns to combine the base model predictions to make a final classification decision.
Final Prediction: The base models generate predictions for the new input data during prediction. These predictions are then used as input features for the meta-classifier, which produces the final classification prediction.

Example

Code Description

Defining Base Models:

The code defines a list base_models that will store the base models for the stacking ensemble. A loop creates 5 different decision tree models (DecisionTree CLassifier) and appends them to the list, each with a unique identifier. Another loop creates 3 SVM (SVC)models with probability estimates enabled.

Defining Meta-Classifier:

A neural network classifier (MLPClassifier) is defined as a meta-classifier. It has two hidden layers with 100 and 50 neurons, respectively. This neural network will learn how to combine predictions from the base models.

Creating Stacking Ensemble:

A StackingClassifier is initialized with the list of base models and the defined meta-classifier. This ensemble model will learn how to leverage the strengths of individual models to make final predictions.

Training. making predictions and evaluating the results:

The stacking classifier is trained using training data to combine predictions from base models with the meta-classifier cleverly. After training, it predicts outcomes for testing data (X_test). To understand how well it performs, the F1 score is calculated by comparing predicted labels (y_pred) to actual labels (y_test), which helps measure accuracy, especially when classes are imbalanced.

What is the purpose of a meta-classifier in a stacking ensemble?

Select the correct answer

Everything was clear?

Section 4. Chapter 1