Autofhm: A Python Library for Automated Machine Learning

Abstract

After the introduction of machine learning, it has gone through lots of research and development which resulted in an explosion of usage in many fields. Developing such a model is not an easy task and it requires extensive domain knowledge and skills. This paper presents Autofhm, a python library used for automated machine learning. This tool automates the steps followed for the machine learning model creation such as feature engineering, model selection, and hyperparameter optimization. For a given dataset, Autofhm generates new deeper features which could increase the performance of the model. Then it selects the best performing model along with the suitable hyperparameter combinations based on the feature engineered dataset. The Autofhm is tested on 5 classification tasks and 5 regression tasks and the results demonstrate that, Autofhm gives good results with lesser time when compared to state-of-the-art frameworks like TPOT.