Modeling species? geographic distributions is an important problem in conservation biology. In this example we model the geographic distribution of two south american

In this example we compare the various initialization strategies for K-means in terms of runtime and quality of the results.

An illustration of the metric and non-metric MDS on generated noisy data. The reconstructed points using the metric MDS and non metric MDS are slightly shifted

This example illustrates that GPR with a sum-kernel including a WhiteKernel can estimate the noise level of data. An illustration

When working with covariance estimation, the usual approach is to use a maximum likelihood estimator, such as the

This example fits an AdaBoosted decision stump on a non-linearly separable classification dataset composed of two ?Gaussian quantiles? clusters (see

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

Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the

This example illustrates the differences between univariate F-test statistics and mutual information. We consider 3 features x_1, x_2, x_3

Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented