4 Hyper-Parameter Tuning Techniques and Limitations
Popular hyper-parameter tuning techniques that every Data Scientist should know
Published in
4 min readSep 4, 2020
Introduction
Wikipedia states that “Hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm”
One of the most challenging parts in ML workflow is finding the best hyperparameters for the model. Performance of ML models is directly related to Hyper-parameter’s. The more you tune the hyperparameters, the better model you get. Tuning Hyper-parameters could be tedious, complicated and is more of an art than science.
Hyper-parameters
Hyper-parameters are the parameters used to control the behaviour of the algorithm while building the model. These parameters cannot be learned from the normal training process. They need to be assigned before training of the model.
Table of Content
- Traditional or Manual Tuning
- Grid Search
- Random Search
