Batch Normalization
Machine Learning
An ensemble technique using bootstrap sampling and aggregation.
Detailed Explanation
Bagging, short for Bootstrap Aggregating, is an ensemble method in machine learning that improves model stability and accuracy. It involves creating multiple training datasets through bootstrap sampling (random sampling with replacement), training a separate model on each, and then aggregating their predictions, typically by voting or averaging. This technique reduces overfitting and variance, enhancing predictive performance.
Use Cases
- •Enhances stock prediction accuracy by aggregating multiple models trained on varied sampled datasets to reduce overfitting and improve stability.
Related Terms
Other terms in the Machine Learning category