sklearn.metrics.log_loss¶
- sklearn.metrics.log_loss(y_true, y_pred, eps=1e-15, normalize=True, sample_weight=None)[source]¶
Log loss, aka logistic loss or cross-entropy loss.
This is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of the true labels given a probabilistic classifier’s predictions. For a single sample with true label yt in {0,1} and estimated probability yp that yt = 1, the log loss is
-log P(yt|yp) = -(yt log(yp) + (1 - yt) log(1 - yp))Read more in the User Guide.
Parameters: y_true : array-like or label indicator matrix
Ground truth (correct) labels for n_samples samples.
y_pred : array-like of float, shape = (n_samples, n_classes)
Predicted probabilities, as returned by a classifier’s predict_proba method.
eps : float
Log loss is undefined for p=0 or p=1, so probabilities are clipped to max(eps, min(1 - eps, p)).
normalize : bool, optional (default=True)
If true, return the mean loss per sample. Otherwise, return the sum of the per-sample losses.
sample_weight : array-like of shape = [n_samples], optional
Sample weights.
Returns: loss : float
Notes
The logarithm used is the natural logarithm (base-e).
References
C.M. Bishop (2006). Pattern Recognition and Machine Learning. Springer, p. 209.
Examples
>>> log_loss(["spam", "ham", "ham", "spam"], ... [[.1, .9], [.9, .1], [.8, .2], [.35, .65]]) 0.21616...
