Ai Regression — Free AI & ML Tutorial
Learn Ai Regression in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ai Regression in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Ai Regression in AI & ML
Regression is one of the oldest and most useful ML techniques. Linear Regression predicts a continuous number (price, temperature, marks). Logistic Regression predicts a category (pass/fail, spam/not-spam) — despite the name, it's a classification algorithm.
Linear Regression: finds the straight line (y = mx + c) that best fits the data. The line minimises the sum of squared errors between predictions and actual values. This is called "Ordinary Least Squares" (OLS).
Logistic Regression: applies a sigmoid function to the linear output, squashing it to 0–1. This gives a probability. If probability > 0.5 → Class 1 (positive), else Class 0 (negative).
When to use what: Continuous output (price, marks, temperature) → Linear Regression. Binary outcome (pass/fail, buy/not-buy, disease/no-disease) → Logistic Regression. Multiple categories → Multiclass Logistic Regression or other classifiers.
Ai Regression — Syntax
# Linear Regression: y = m*x + c # m (slope): how much y changes per unit x # c (intercept): value of y when x = 0 # # Logistic Regression: # z = m*x + c # probability = 1 / (1 + e^(-z)) # if probability > 0.5: class=1 else class=0
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