Logistic Regression

Harvard EPS-210 | Interactive tutorial — Click to add data points and watch the model learn

Classification Space

Class 0
Class 1
Decision Boundary

Sigmoid Function

σ(z) = 1 / (1 + e-z)
z = w₀ + w₁x₁ + w₂x₂

Probability Surface

Training Progress

Add Data Points

Click on the classification canvas to add points

Manual Weights

Bias (w₀) 0.00
Weight x₁ (w₁) 0.00
Weight x₂ (w₂) 0.00

Training Settings

Learning Rate 0.50

Model Statistics

Accuracy
--
Loss
--
Class 0
0
Class 1
0

Quick Examples

How it works: Logistic regression models the probability that a point belongs to class 1 using the sigmoid function. The decision boundary is where P(y=1) = 0.5.