A tiny logistic model learns to classify objects as Jets or Sharks using simple features. Inspired by classic PDP examples.
What to do: Click Train model to let the network learn from its labelled examples. Once trained, toggle the feature checkboxes on the left and watch how the Jet probability on the right changes. Pay attention to the learned weights in the table.
Think about: Which features received the largest positive or negative weights? A large positive weight means the feature pushes the prediction toward "Jet"; a large negative weight pushes it toward "Shark." This is how even a simple network captures statistical patterns in data — the same principle behind larger neural networks in modern AI.
| Feature | Weight |
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