Ensemble Method
2024 | AI Dictionary
What is an Ensemble Method: Technique combining multiple models to improve prediction accuracy and robustness in machine learning applications.
What is an Ensemble Method?
An Ensemble Method is a machine learning approach that combines multiple models to improve predictive accuracy and robustness . By aggregating the outputs of various models, ensemble methods reduce errors, as they balance individual model biases and variances, producing more reliable outcomes.
Types of Ensemble Methods
- Bagging: Uses random subsets of training data to create multiple models (e.g., Random Forest ) and averages their predictions, reducing variance.
- Boosting: Sequentially trains models, emphasizing misclassified instances to reduce bias (e.g., AdaBoost, Gradient Boosting).
- Stacking: Combines the predictions of several base models with a meta-model, enhancing predictive performance.
Applications of Ensemble Methods
- Fraud Detection: Aggregates predictions from different models to improve fraud detection accuracy.
- Medical Diagnosis: Increases diagnostic reliability by combining insights from multiple classifiers.
- Stock Market Forecasting: Improves prediction stability by combining various forecasting models.
Example of an Ensemble Method
An example of an ensemble method is Random Forest, which creates multiple decision trees and averages their predictions to reduce overfitting and improve generalization in classification and regression tasks.
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