Common Mistakes in Hyper-Parameters Tuning

Web: https://towardsdatascience.com/common-mistakes-in-hyper-parameters-tuning-ff5951e6c2d?source=rss----7f60cf5620c9---4

Hyper-parameter tuning for machine learning models is a trial and error game. To succeed, it is best to avoid the following mistakes.

Starting from a given dataset, training a machine learning model implies the computation of a set of model parameters that minimizes/maximizes a given metric or optimization function. The optimum point is generally found using a gradient descent-based method.

However, most models are defined with an extra layer of parameters, called hyper-parameters …

data scientist hyper-parameter-tuning mistakes