An Easy Guide to Understanding Ensemble Methods

In the context of Machine Learning, Ensemble Methods is a technique that combines the predictions of multiple individual models (learners) to produce a more accurate and robust prediction than any single model on its own.

The fundamental idea behind ensemble methods is that by aggregating the predictions of diverse models, the collective intelligence can often outperform individual models and provide better results.

Types of Ensemble Methods

1) Bagging (Bootstrap Aggregating): Involves training multiple instances of the same model on different subsets of the training data, often using bootstrapping (sampling with replacement). The final prediction is typically the average (for regression) or the majority vote (for classification) of the individual model predictions. Random Forest is a popular example of a bagging ensemble.

1.1) Random Forest: Random Forest is an ensemble learning method used for both classification and regression tasks. It works by combining the outputs of multiple decision trees to improve the overall accuracy and reduce overfitting.

Here’s how Random Forest works step by step:

Step 1: Data Preparation

• Start with a dataset containing input features and target labels.
• Split the data into a training set and a test set