Member-only story

Hyperparameter Tuning and Model Selection

Learn how to tune hyperparameters and choose the best model for a given problem

ML Musings
4 min readFeb 6, 2023
Photo by Michael Dziedzic on Unsplash

Hyperparameter tuning and model selection are crucial steps in the machine-learning pipeline. These processes help to optimize the performance of the model by finding the best combination of hyperparameters and choosing the appropriate model for the problem at hand. In this article, we will discuss hyperparameter tuning and model selection in detail.

Hyperparameter Tuning

Hyperparameters are the parameters of a machine learning model that are set before training the model. They control the behavior and performance of the model. Examples of hyperparameters include the learning rate in gradient descent, the regularization coefficient, and the number of hidden layers in a neural network.

Hyperparameter tuning refers to the process of finding the best combination of hyperparameters that leads to the best performance of the model. There are several ways to perform hyperparameter tuning:

Grid Search

Grid search involves exhaustively searching over a pre-defined set of hyperparameters. For example, if we have two hyperparameters, learning rate, and regularization coefficient, we can specify a grid of…

--

--

ML Musings
ML Musings

Written by ML Musings

✨ I enjoy pushing the boundaries of JS, Python, SwiftUI and AI. You can support my work through coffee - www.buymeacoffee.com/MLMusings

No responses yet