boostingtekniikoita
Boostingtekniikoita, or boosting techniques, are ensemble learning methods used in machine learning and statistics to improve predictive performance by combining multiple weak learners into a single strong learner. The fundamental idea is to train learners sequentially, each focusing on the mistakes made by its predecessors. By iteratively reducing bias or variance, boosting achieves high accuracy even with simple underlying models such as decision stumps or shallow decision trees.
Early boosting algorithms include AdaBoost, introduced in 1995, which adjusts sample weights based on classification errors
Boosting is particularly effective on tabular data and problems requiring high accuracy, such as credit scoring,
In practice, boosting techniques are widely applied across industry and academia, providing superior performance in many