What Ensemble Learning Meaning, Applications & Example
A technique that combines multiple models for better performance.
What is Ensemble Learning?
Ensemble Learning is a machine learning technique that combines multiple models to improve accuracy and robustness . By aggregating the predictions of several models, ensemble methods reduce variance and bias, often resulting in more reliable performance than individual models.
Types of Ensemble Learning
- Bagging: Creates multiple models from random subsets of the training data (e.g., Random Forests) to reduce variance.
- Boosting: Sequentially trains models, giving more weight to misclassified instances in each iteration (e.g., AdaBoost, Gradient Boosting) to reduce bias.
- Stacking: Combines predictions from several models, using another model (meta-learner) to make the final prediction, which can improve overall accuracy.
Applications of Ensemble Learning
- Fraud Detection: Detects anomalies by combining predictions from various models to improve fraud detection rates.
- Medical Diagnosis: Enhances diagnosis accuracy by aggregating predictions from multiple classifiers analyzing patient data.
- Financial Forecasting: Improves the reliability of stock and market predictions by combining multiple forecasting models.
Example of Ensemble Learning
An example of Ensemble Learning is in spam email detection, where ensemble methods like Random Forests and Gradient Boosting combine the strengths of multiple classifiers to better identify and filter spam emails, reducing the risk of false positives and negatives.