Comparing Linear Regression and Logistic Regression: Unveiling Differences with Examples
INTRODUCTION
Linear Regression and Logistic Regression are two fundamental algorithms in the field of machine learning and statistics. While they both involve regression, they serve distinct purposes and have key differences. In this article, we’ll conduct a comprehensive comparison between Linear Regression and Logistic Regression using a table format, along with illustrative examples.
Comparison between Linear Regression and Logistic Regression
AspectLinear RegressionLogistic RegressionPurposePredicting continuous values (regression)Predicting categorical classes (classification)OutputContinuous values (e.g., house prices)Probability of belonging to a class (0 or 1)Equationy = b0 + b1x + … + bnxnP(Y=1) = 1 / (1 + e^(-z))AssumptionLinear relationship between features and targetLog-odds of success is linearly related to features output InterpretationChange in the target for a unit change in featureProbability of belonging to the positive classExamplePredicting house prices based on area predicting whether an email is spam or not
