Regression and Classification in Supervised Learning
Jiachong Li · 2019
The problem of recognizing patterns from big data has attracted a lot of attention these days, especially in artificial intelligence and machine learning fields. People are interested in training computers to make predictions or classifications on their own based on past experience, i.e., data. In this paper, we review three fundamental supervised learning models (linear regression, logistic regression, and perceptron) for both regression and classification tasks, including their theoretical background, algorithmic solutions, and application scenarios. We also conduct synthetic experiments to demonstrate their performance.