By Neeta Ganamukhi

Neeta Ganamukhi着

Department of Business and Economics

商业与经济系

Abstract: The main aim of this term paper is to describe the Logistic Regression Algorithm, a supervised model used for classification. The paper describes Logistic Regression for Machine Learning, types of Logistic Regression and hypothesis, multinomial and ordinal are not covered in this paper. The paper also covers Sigmoid function, Decision Boundary Data Preparation, Cost function, Gradient descent, Difference between linear and logistic regression, and Pros and cons of Logistic Regression.

摘要：本学期论文的主要目的是描述Logistic回归算法，这是一种用于分类的监督模型。 本文描述了机器学习的Logistic回归，本文不涉及Logistic回归和假设的类型，多项式和序数。 本文还介绍了Sigmoid函数，决策边界数据准备，成本函数，梯度下降，线性和