Explainable Machine Learning Models and Architectures

1A Comprehensive Review of Various Machine Learning Techniques

Pooja Pathak* and Parul Choudhary^†

Dept. of Computer Engineering and Applications, GLA University, Mathura, India

Abstract

The creation of an intelligent system that works like a human is due to Artificial intelligence (AI). It can be broadly classified into four techniques: machine learning, machine vision, automation and Robotics and natural language processing. These domains can learn from data provided, identify the hidden pattern and make decisions with human intervention. There are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Thus, to reduce the risk factor while decision making, machine learning techniques are more beneficial. The benefit of machine learning is that it can do the work automatically, once it learns what to do. Therefore, in this work, we discuss the theory behind machine learning techniques and the tasks they perform such as classification, regression, clustering, etc. We also provide a review of the state of the art of several machine learning algorithms like Naive Bayes, random forest, K-Means, SVM, etc., in detail.

Keywords: Machine learning, classification, regression, recognition, clustering, etc.

1.1 Introduction

Machine learning spans IT, statistics, probability, AI, psychology, neurobiology, and other fields. Machine learning solves problems by creating a model that accurately represents a dataset. Teaching computers to ...