This example compares 2 dimensionality reduction strategies: univariate feature selection with Anova feature

class sklearn.gaussian_process.kernels.PairwiseKernel(gamma=1.0, gamma_bounds=(1e-05, 100000.0), metric='linear',

This example demonstrates the problems of underfitting and overfitting and how we can use linear regression with polynomial features to approximate nonlinear functions

Compares FeatureHasher and DictVectorizer by using both to vectorize text documents. The example demonstrates syntax and speed only; it doesn

This example demonstrates the behavior of Gaussian mixture models fit on data that was not sampled from a mixture of Gaussian random variables. The dataset

class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski'

Illustration of how the performance of an estimator on unseen data (test data) is not the same as the performance on training data. As the regularization increases

sklearn.cluster.ward_tree(X, connectivity=None, n_clusters=None, return_distance=False)

sklearn.datasets.make_friedman2(n_samples=100, noise=0.0, random_state=None)

class sklearn.neighbors.LSHForest(n_estimators=10, radius=1.0, n_candidates=50, n_neighbors=5, min_hash_match=4, radius_cutoff_ratio=0