Ensemble Selection from Libraries of Models

This paper describes a method for building ensembles of machine learning models called ensemble selection, which uses a forward stepwise selection process to identify the best subset of models from a large library of models trained with different algorithms and parameters. The authors demonstrate that ensemble selection outperforms other ensemble methods, such as bagging and boosting, on a variety of metrics and data sets. The effectiveness of ensemble selection is attributed to its ability to optimise ensembles to specific performance metrics while mitigating overfitting through techniques like bagged ensemble selection and sorted ensemble initialisation.

Find the paper: https://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf

All Images by NosazenaNFT: https://linktr.ee/nosazenanfts

Nosacapital Square (On Binance): https://www.binance.com/en/square/profile/NosaCapital

Open a Free Morpher Account (and get free tokens): https://nosax.me/morpher

#nosacapital #machinelearning #machinelearningmodels #ensemble