Logistic Regression

Machine Learning

December 29, 2020

Why is the Sigmoid function used for Logistic Regression?

Logistic regression deals with with binary class problems and in it we try to predict in which of the two classes the data point belongs. Secondly we compute this probability using the distance of the point from the line that separates the the two classes. In other words the prediction accuracy depends upon the slope and intercept value of the aforementioned line.

The attributes of sigmoid function that makes it best suited for logistic regression algorithm are:

1. Accentuation: Due exponential nature of the curve described by sigmoid function, a small change in input results in large change in output. Therefore for a small variation in the slope or intercept of the classification line, the change in error rate is significant. This helps improve model accuracy and ensures generalization.
2. Range: The range of the function is between 0 & 1, thus making it a suitable candidate for probability computations.

by : Monis Khan

Quick Summary:

Logistic regression deals with with binary class problems and in it we try to predict in which of the two classes the data point belongs. Secondly we compute this probability using the distance of the point from the line that separates the the two classes. In other words the prediction accuracy depends upon the slope […]