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This article delves into the principles of logistic regression, including its types and implementations.
What is Regression ?
It predicts the continuous output variables using the independent input variable.
Difference Between Linear Regression & Logistic Regression
Linear regression is a supervised machine learning approach that determines the linear connection between the dependent variable and one or more independent features by applying a linear equation to the observed data. The data here is continuous.
- Simple Linear Regression occurs when there is just one independent feature.
- Multiple Linear Regression occurs when there are multiple features.
- Univariate Linear Regression is when there is only one dependent variable.
- Multivariate Regression is when there are multiple dependent variables.
Logistic regression is a supervised machine learning technique used in classification problems to predict whether an instance belongs to a specified class or not. Logistic regression is a statistical procedure that focuses on the relationship between two data variables. The data here is categorical or binary implying that the output can only be represented in the case of 0 or 1 , yes or no , true or false etc.
