sklearn.metrics.log_loss(y_true, y_pred, eps=1e-15, normalize=True, sample_weight=None, labels=None)

sklearn.metrics.pairwise.sigmoid_kernel(X, Y=None, gamma=None, coef0=1)

sklearn.datasets.make_spd_matrix(n_dim, random_state=None)

This example demonstrates how to generate a dataset and bicluster it using the Spectral Co-Clustering algorithm. The dataset is generated

This example shows how kernel density estimation (KDE), a powerful non-parametric density estimation technique, can be used to learn a generative model for a dataset

Illustration of the effect of different regularization strategies for Gradient Boosting. The example is taken from Hastie et al 2009. The loss function

class sklearn.covariance.LedoitWolf(store_precision=True, assume_centered=False, block_size=1000)

sklearn.model_selection.fit_grid_point(X, y, estimator, parameters, train, test, scorer, verbose, error_score='raise', **fit_params)

Datasets Scikit-learn deals with learning information from one or more datasets that are represented as 2D arrays. They can be understood as a list of multi-dimensional observations

Gaussian Processes (GP) are a generic supervised learning method designed to solve regression and probabilistic classification problems