Brier score
Topic of Logistic regression, part of Statistical learning
Brier score
| Type | Topic |
| Taught in | Logistic regression |
| Part | Statistical learning |
The mean squared difference between predicted probabilities and the 0–1 outcomes, \frac1n\sum_i(\hat p_i-y_i)^2. It rewards calibrated probabilities, not just correct classifications.
In Logistic regression
From Logistic regression
Setosa is easy to separate on these measurements, so a three-class fit would hide the binary difficulty. Restrict training to versicolor and virginica and use sepal length alone. Set Y=1 for virginica, so a positive coefficient means larger modelled odds of virginica. The probability threshold 1/2 is the equal-cost rule. The Brier score below scores the probabilities themselves.
Used in
See also
Binary logistic regression · A finite stationary point is a global maximiser · From a linear model to class probabilities · Logistic and probit models · Likelihood, score, and fitting · Score and Hessian of logistic regression · Iteratively reweighted least squares · Interpretation and the decision boundary