Glossary · Phase 3 · Machine learning

Logistic regression

A simple classifier: a weighted sum of inputs squeezed into a probability between 0 and 1.

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Train the model, then slide the threshold. Training changes what it knows; the threshold only changes how cautious it is.

Logistic regression · Predict probabilities for yes/no outcomes. About 30 minutes.

coefficient: Another name for a weight in a linear or logistic regression: how much one feature pushes the prediction.
decision boundary: The line (or surface) where a model switches from answering yes to answering no.
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.
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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