ML0048 Logistic Regression

Logistic regression maps inputs to a probability space for classification, while linear regression estimates continuous outcomes through a direct linear relationship.

Logistic regression model estimates the probability that a binary outcome (y = 1) occurs, given an input vector (x)

Where:
is the input feature vector,
is the weight vector, and
is the bias term.

Logistic Regression vs. Linear Regression:
Linear Regression:
Purpose: Predicts a continuous output (e.g., price, height).
Output: Real number (can be negative or >1).
Assumes: Linearity between input features and output.

Logistic Regression:
Purpose: Predicts a probability for classification (e.g., spam or not).
Output: Value between 0 and 1 using sigmoid function.
Interpreted as: Probability of class membership.

Here is a table comparing Logistic Regression with Linear Regression.