Linear and Logistic Regression

Wed, 01 Jul 2020 00:00:00 +0000

Linear and Logistic regression are among the most elementary algorithms for supervised learning. Supervised Learning describes the situation where we deal with labeled data, which means that we have labeled inputs and a target variable.

Despite the fact that both have the word “regression” in their name, only one of them is typically being used for solving regression problems!

Let’s see how they work!

Linear Regression

Linear regression is possibly the easiest, most intuitive way of making a quantitative prediction. The relationship between an independent and a dependent variable is assumed to be linear, meaning that the dependent variable can be predicted using a linear function of the independent variable. For example:

Regression on Gabriel Berardi

Linear and Logistic Regression

Linear Regression