Rethinking default values: A low cost and efficient strategy to define hyperparameters
Machine Learning (ML) algorithms have been increasingly applied to problems from several
different areas. Despite their growing popularity, their predictive performance is usually
affected by the values assigned to their hyperparameters (HPs). As consequence,
researchers and practitioners face the challenge of how to set these values. Many users
have limited knowledge about ML algorithms and the effect of their HP values and, therefore,
do not take advantage of suitable settings. They usually define the HP values by trial and …
different areas. Despite their growing popularity, their predictive performance is usually
affected by the values assigned to their hyperparameters (HPs). As consequence,
researchers and practitioners face the challenge of how to set these values. Many users
have limited knowledge about ML algorithms and the effect of their HP values and, therefore,
do not take advantage of suitable settings. They usually define the HP values by trial and …
Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters
R Gomes Mantovani, AL Debiaso Rossi… - arXiv e …, 2020 - ui.adsabs.harvard.edu
Abstract Machine Learning (ML) algorithms have been increasingly applied to problems
from several different areas. Despite their growing popularity, their predictive performance is
usually affected by the values assigned to their hyperparameters (HPs). As consequence,
researchers and practitioners face the challenge of how to set these values. Many users
have limited knowledge about ML algorithms and the effect of their HP values and, therefore,
do not take advantage of suitable settings. They usually define the HP values by trial and …
from several different areas. Despite their growing popularity, their predictive performance is
usually affected by the values assigned to their hyperparameters (HPs). As consequence,
researchers and practitioners face the challenge of how to set these values. Many users
have limited knowledge about ML algorithms and the effect of their HP values and, therefore,
do not take advantage of suitable settings. They usually define the HP values by trial and …
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