Ensemble Classifier

Ensemble Classifier using Decision Trees and Random Forest

This code demonstrates building an ensemble classifier by utilizing individual Decision Trees and a Random Forest Classifier.

Methodology

Individual Decision Trees

• The code creates multiple Decision Trees by performing bootstrap sampling on the given dataset.
• Bootstrap sampling generates random subsets of the data for each Decision Tree.
• Categorical columns in the dataset are label-encoded for processing.
• For each split, the code trains an individual Decision Tree on the respective sampled data.
• The accuracy for each individual Decision Tree is calculated and displayed.

Random Forest Classifier

• The code constructs a Random Forest Classifier using the previously trained individual Decision Trees.
• The number of estimators for the Random Forest equals the number of splits/individual Decision Trees.
• The Random Forest Classifier is fitted using the training data.

Evaluation

• The Random Forest Classifier makes predictions on the test dataset.
• Accuracy is calculated for the Random Forest Classifier.
• A confusion matrix is generated to assess the performance of the Random Forest.

File Overview

• EnsembleClassifier.py: Python code implementing the ensemble classifier.
• README.md: Documentation providing an overview of the ensemble classifier code.

Usage

Running the Code

• The code can be executed to build individual Decision Trees and create a Random Forest Classifier.
• The user can input the number of splits to create individual Decision Trees.

Implementation Details

• The code reads a dataset from a Google Drive link and utilizes it to build the ensemble classifier.
• Categorical columns are label-encoded, and the dataset is split into training and test sets for evaluation.

Results

• The code displays the accuracy for each individual Decision Tree and the overall accuracy for the Random Forest Classifier.
• The confusion matrix provides insights into the performance of the Random Forest.

Summary

The provided code showcases the creation of an ensemble classifier by training individual Decision Trees using bootstrapped random subsets of the given dataset. It then assembles these trees into a Random Forest Classifier, demonstrating the power of ensemble learning in classification tasks.