Logistic regression
Shading shows the model's probability for each spot: blue for class A, orange for class B, gray where it is unsure. Tap to add points.
- Class A
- Class B
- Decision boundary (50%)
- 10% and 90% lines
Steps0
Mean cross-entropy0.6931
Accuracy50%
Weights w1, w2, b0, 0, 0
Try this
- Train on Separated data and watch the 10% and 90% lines close in on the boundary.
- Switch to Overlapping and see where the loss levels off.
- Add points of one class inside the other’s region and retrain.
The one-input version, with sliders for the weight and bias:
From score to probability
Made-up data: hours of practice before a driving test, and whether the test was passed. The curve is the model's probability of passing.
- Failed (y = 0)
- Passed (y = 1)
- Gap to the right answer (thickest: the costliest point)
Mean cross-entropy0.378
Accuracy at 50%81%
50% point2.5 hours