The goal of this guide is to explore some of the main scikit-learn tools on a single practical task: analysing a collection of text documents (newsgroups

Demonstrates the effect of different metrics on the hierarchical clustering. The example is engineered to show the effect of the choice

class sklearn.neighbors.NearestNeighbors(n_neighbors=5, radius=1.0, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None

class sklearn.cluster.KMeans(n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None

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

Plot the decision boundaries of a VotingClassifier for two features of the Iris dataset. Plot the class probabilities

sklearn.metrics.v_measure_score(labels_true, labels_pred)

An illustration of the isotonic regression on generated data. The isotonic regression finds a non-decreasing approximation of a function while minimizing the mean squared

class sklearn.feature_selection.SelectPercentile(score_func=, percentile=10)

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