Ensemble Learning

  Bagging (Bootstrap Aggregation): The term bagging originates from "b" in bootstrap and "agg" in aggregation . It is widely used in statistics and machine learning to improve the performance and robustness of predictive models. What is Bagging? Bagging is an ensemble technique that creates multiple models by training them on different subsets of the dataset and then aggregates their predictions to produce a more stable and accurate output. One of the most popular bagging models is the Random Forest , which leverages decision trees as base models. How Bagging Works: Training Data Subsets: Given a dataset D n ​, random samples of size m​ are drawn with replacement. Each sample forms a unique training subset for creating a model m i . This process is repeated k times, resulting in k models: m 1 + m 2 + m 3 + …… + m k . Model Aggregation: For classification , predictions are aggregated using a majority vote . Fo...

  What is Ensemble Learning? The term ensemble means "a collection of things or a group of things."  In the context of machine learning, ensemble learning refers to the technique of combining multiple models to create a stronger, more accurate, and robust predictive system than any single model could achieve alone. Real life scenario..... To understand ensemble learning better, think about how different musicians with unique instruments come together to compose a beautiful symphony(Fig:- 1). Similarly, multiple machine learning models with diverse strengths are combined to produce better predictions(Fig:- 2). Fig:- 1 In Machine Learning Fig:- 2 Key Concepts of Ensembles Base Models (M1, M2, M3, ... Mn): These are the individual models that form the building blocks of the ensemble. Th e unique contributions of each model are combined to create a powerful and accurate predictive system. Why Combine Models? No single model is perfect. By combining the predictions of multiple m...