When performing classification you often want to predict not only the class label, but also the associated probability. This probability gives you some

This example visualizes some training loss curves for different stochastic learning strategies, including SGD and Adam. Because of time-constraints

Plot the maximum margin separating hyperplane within a two-class separable dataset using a linear Support Vector Machines classifier trained using SGD

Clustering: grouping observations together The problem solved in clustering Given the

sklearn.model_selection.permutation_test_score(estimator, X, y, groups=None, cv=None, n_permutations=100, n_jobs=1

The

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

sklearn.metrics.explained_variance_score(y_true, y_pred, sample_weight=None, multioutput='uniform_average')

This example is meant to illustrate situations where k-means will produce unintuitive and possibly unexpected clusters. In the first three plots, the input

Support vector machines (SVMs) are a set of supervised learning methods used for