Instructions:
• Select different Decision Boundary Types to handle nonlinear data patterns
• Adjust the parameters shown below the boundary type (different parameters appear for different types)
• Use the "Click adds" dropdown to select which class to add, then click anywhere on the plot to add data points
• Use "Generate New Data" to create random 2D classification data
• Use "Clear All Data" to remove all points
• Find Optimal Solution: Optimizes parameters for the selected boundary type

Boundary Types & Parameters:
• Linear: Uses w₁, w₂, b → Straight line boundary
• Quadratic: Uses w₁, w₂, b, w₁₁, w₂₂, w₁₂ → Curved boundaries (ellipses, parabolas)
• Circular: Uses radius, center X₁, center X₂ → Perfect circles

Visualization:
• Solid line: Decision boundary (50% probability - where the model is most uncertain)
• Dotted lines: Probability contours at 25% and 75% - show regions of high confidence for each class

Metrics:
• Accuracy: Percentage of correctly classified points
• Loss: Cross-entropy loss, the standard loss function for classification