Ensemble learning is a machine learning paradigm that involves combining the predictions of multiple models to improve overall performance, accuracy, and robustness. The idea is to leverage the strengths of individual models and compensate for their weaknesses, ultimately achieving better generalization and predictive power than any single model.
Here are key concepts and characteristics of ensemble learning:
I. Diversity of Models:
- Ensemble methods often work best when the individual models (learners or base models) are diverse. Diversity is achieved by training models using different algorithms, subsets of data, or variations in model parameters.
II. Types of Ensemble Learning:
1. Bagging (Bootstrap Aggregating):
- In bagging, multiple instances of a model are trained independently on different subsets of the training data, often created through bootstrapping (random sampling with replacement). The final prediction is typically an average or a vote among the individual model predictions. Random Forest is a well-known bagging algorithm.
from sklearn.ensemble import…