Glossary · Phase 3 · Machine learning
Decision boundary
The line (or surface) where a model switches from answering yes to answering no.
A customer with a score of exactly 0 gets exactly 0.5. They sit on the fence. All the fence-sitters together form the decision boundary. On one side the model leans "yes". On the other side it leans "no".
Try it now
Train the model, then slide the threshold. Training changes what it knows; the threshold only changes how cautious it is.
Learn it properly
Logistic regression · Predict probabilities for yes/no outcomes. About 30 minutes.
Other terms from the same lesson
- coefficient: Another name for a weight in a linear or logistic regression: how much one feature pushes the prediction.
- log-odds: The natural log of the odds. Logistic regression's score is a log-odds: 0 means 50/50, positive leans yes, negative leans no.
- logistic regression: A simple classifier: a weighted sum of inputs squeezed into a probability between 0 and 1.
- odds: The chance something happens divided by the chance it doesn't. A 75% chance is odds of 3 (3 to 1).
- odds ratio: How much the odds get multiplied when one feature goes up by 1. Above 1 raises the odds; below 1 lowers them.
- sigmoid: An S-shaped function that squeezes any number into the range 0 to 1.
- threshold: A cutoff value: on one side the answer is yes, on the other no. A classifier often says yes above a probability of 0.5.
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