Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace (China), reducing the number of colors required to show the image from 96,615

A simple one-dimensional regression example computed in two different ways: A noise-free case

Density estimation walks the line between unsupervised learning, feature engineering, and data modeling. Some of the most popular and useful density estimation techniques

class sklearn.linear_model.SGDClassifier(loss='hinge', penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, n_iter=5,

sklearn.metrics.pairwise_distances_argmin_min(X, Y, axis=1, metric='euclidean', batch_size=500, metric_kwargs=None)

Datasets can often contain components of that require different feature extraction and processing pipelines. This scenario might occur when:

sklearn.model_selection.cross_val_predict(estimator, X, y=None, groups=None, cv=None, n_jobs=1, verbose=0, fit_params=None

Sample usage of Nearest Centroid classification. It will plot the decision boundaries for each class.

Every estimator has its advantages and drawbacks. Its generalization error can be decomposed in terms of bias, variance and noise. The

Computes a Bayesian Ridge Regression on a synthetic dataset. See