Oss - scikit-learn

sklearn.metrics .log_loss¶ 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. The log loss is only defined for two or more labels. For a single sample with true label yt in and estimated probability yp that yt = 1, the log loss is. Read more in the User Guide . Ground truth (correct) labels for n_samples samples. y_pred : array-like of float, shape = (n_samples, n_classes) or (n_samples,) Predicted probabilities, as returned by a classifier’s predict_proba method. If y_pred.shape = (n_samples,) the probabilities provided are assumed to be that of the positive class. The labels in y_pred are assumed to be ordered alphabetically, as done by preprocessing.LabelBinarizer . 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. labels : array-like, optional (default=None) If not provided, labels will be inferred from y_true. If labels is None and y_pred has shape (n_samples,) the labels are assumed to be binary and are inferred from y_true . .. versionadded:: 0.18. The logarithm used is the natural logarithm (base-e).