{
  "id": "logistic-regression",
  "title": "Logistic regression from scratch",
  "kind": "implement",
  "summary": "A stable sigmoid, the binary cross-entropy loss and its gradient, and a gradient descent loop that records the loss.",
  "difficulty": "medium",
  "tags": ["logistic-regression", "classification", "gradients", "numerical-stability"],
  "lesson": "phases/02-ml-fundamentals/03-logistic-regression",
  "cheatsheet": "classical-ml",
  "minutes": 25,
  "entry": ["sigmoid", "compute_loss", "compute_gradients", "fit"],
  "collections": ["classical-ml"],
  "time_limit_s": 10,
  "mutants": {
    "bias_not_updated": "Computes the bias gradient but never updates the bias in fit.",
    "grad_sum_not_mean": "Sums the weight gradient over the examples and does not divide by n.",
    "grad_wrong_sign": "Uses y - p and not p - y, so the gradient points uphill.",
    "history_before_update": "Records the loss before the gradient step and not after it.",
    "loss_log_of_sigmoid": "Takes the log of the sigmoid output, so a confident wrong prediction gives an infinite loss.",
    "sigmoid_branches_swapped": "Swaps the two branches of the stable sigmoid, so the output is 1 - sigmoid(z)."
  }
}