Logistic Regression - ML Cheatsheet documentation

Logistic Regression¶ Introduction¶ Logistic regression is a classification algorithm used to assign observations to a discrete set of classes. Unlike linear regression which outputs continuous number values, logistic regression transforms its output using the logistic sigmoid function to return a probability value which can then be mapped to two or more discrete classes. Comparison to linear regression¶ Given data on time spent studying and exam scores. Linear Regression and logistic regression can predict different things: Linear Regression could help us predict the student’s test score on a scale of 0 - 100. Linear regression predictions are continuous (numbers in a range). Logistic Regression could help use predict whether the student passed or failed. Logistic regression predictions are discrete (only specific values or categories are allowed). We can also view probability scores underlying the model’s classifications. Types of logistic regression¶ Binary (Pass/Fail) Multi (Cats, Dogs, Sheep) Ordinal (Low, Medium, High) Binary logistic regression¶ Say we’re given data on student exam results and our goal is to predict whether a student will pass or fail based on number of hours slept and hours spent studying. We have two features (hours slept, hours studied) and two classes: passed (1) and failed (0).