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.


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.


loss : float


The logarithm used is the natural logarithm (base-e).


C.M. Bishop (2006). Pattern Recognition and Machine Learning. Springer, p. 209.


>>> log_loss(["spam", "ham", "ham", "spam"],  
...          [[.1, .9], [.9, .1], [.8, .2], [.35, .65]])

Examples using sklearn.metrics.log_loss