An Efficient Genetic Algorithm based Auto ML Approach for Classification and Regression

Abstract

In recent years, AutoML is booming as the time-consuming and iterative tasks involved in developing a machine learning model can be automated using AutoML. It aims to lessen the requirement for skilled individuals to create the ML model. Additionally, it helps to increase productivity and advance machine learning research. Hence, this paper focusses on developing an AutoML model using genetic algorithm to automatically fulfill the function of network architecture search. The proposed methodology has been evaluated in different scenarios such as binary classification and regression. From the results it is observed that the accuracy achieved for binary classification and regression is 98%.