AlgoViz

Logistic Regression Visualization

Iterations: 0
Learning Rate:
0.010
Add Points:
Show Decision Boundary

Model: P(y=1) = sigmoid(0.000 + 0.000x + 0.000y)

How It Works

Logistic Regression is a supervised learning algorithm used for binary classification problems:

  1. The algorithm learns weights for a linear function: z = w₀ + w₁x + w₂y
  2. This linear function is transformed using the sigmoid function: P(y=1) = 1/(1+e⁻ᶻ)
  3. The result is a probability between 0 and 1, with 0.5 as the decision boundary
  4. Gradient descent is used to find the optimal weights that minimize classification error

Interactive Features

  • Add points of either class by clicking on the canvas
  • Adjust the learning rate to control how quickly the model learns
  • View the decision boundary and probability regions in real-time
  • Generate random points to quickly test the algorithm