What is Ensemble learning?

Ensemble learning is a machine learning technique that involves combining multiple models, called base learners or weak learners. Using ensemble learning builds a more accurate and robust predictive model. The idea behind ensemble learning is that by combining the predictions of multiple models. The resulting ensemble model can achieve better performance than any individual model.

On the other hand, applies to both classification and regression problems. There are several popular ensemble learning methods, including:

  1. Bagging (Bootstrap Aggregating): In bagging, we train multiple base learners independently on different subsets of the training data, which we create by randomly sampling with replacement. Combining the predictions of the base learners is done by averaging or voting to make the final prediction.
  2. Boosting: Boosting algorithms, such as AdaBoost (Adaptive Boosting) and Gradient Boosting, work by training base learners sequentially. Where each subsequent learner focuses on the examples that were misclassified or have high residual errors by the previous learners. We make the final prediction by combining the weighted predictions of the base learners, where we determine the weights based on their performance.
  3. Random Forest: Random Forest is an ensemble method that combines the ideas of bagging and feature randomness.
  4. Stacking: In stacking, the process involves training multiple base learners and subsequently combining their predictions using another model. The base learners’ predictions serve as input features for the meta-learner, which learns to make the final prediction. In stacking, we can perceive it as a two-level learning process, where the base learners learn from the original data, and the meta-learner learns from the predictions made by the base learners.

Ensemble learning helps improve model generalization, reduces overfitting, and increases prediction accuracy by leveraging the diversity and collective wisdom of multiple models. Ensemble learning finds widespread use in various machine learning applications and has achieved great success in solving many real-world problems.





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