Basic Hyperparameters Tuning Methods for Classification Algorithms

Abstract

Considering the dynamics of the economic environment and the amount of data generated every second, the decision-making process is changing and becomes data driven, highly influencing the business strategies setup in order to keep the competitive advantage. However, without technology, data analysis would not be feasible, reason why machine learning is seen as a disruptive innovation for businesses, especially due to its capacity to convert data into actionable outcomes. Though, for a high-quality machine learning model result, algorithm selection and hyperparameters optimization play vital roles, hence became high-interest topics in the field. To achieve this, various automatic selection methods have been proposed and the aim of this paper is to compare two of them – GridSearch and RandomizedSearch - and assess their impact on the model accuracy by comparing with the results obtained when default hyperparameters were applied.

Keywords

• hyperparameters
• tuning
• gridsearch
• randomizedsearch
• classification