sklearn.datasets.fetch_20newsgroups_vectorized(subset='train', remove=(), data_home=None) [source] Load the 20 newsgroups dataset and transform it into tf-idf vectors. This is a convenience function; the tf-idf transformation is done using the default settings for sklearn.feature_extraction.text.Vectorizer. For more advanced usage (stopword filtering, n-gram extraction, etc.), combine fetch_20newsgroups with a custom Vectorizer or CountVectorizer. Read more in the User Guide. Parameters: s

This example plots the ellipsoids obtained from a toy dataset (mixture of three Gaussians) fitted by the BayesianGaussianMixture class models with a Dirichlet distribution prior (weight_concentration_prior_type='dirichlet_distribution') and a Dirichlet process prior (weight_concentration_prior_type='dirichlet_process'). On each figure, we plot the results for three different values of the weight concentration prior. The BayesianGaussianMixture class can adapt its number of mixture componentsau

class sklearn.preprocessing.Binarizer(threshold=0.0, copy=True) [source] Binarize data (set feature values to 0 or 1) according to a threshold Values greater than the threshold map to 1, while values less than or equal to the threshold map to 0. With the default threshold of 0, only positive values map to 1. Binarization is a common operation on text count data where the analyst can decide to only consider the presence or absence of a feature rather than a quantified number of occurrences f

class sklearn.covariance.ShrunkCovariance(store_precision=True, assume_centered=False, shrinkage=0.1) [source] Covariance estimator with shrinkage Read more in the User Guide. Parameters: store_precision : boolean, default True Specify if the estimated precision is stored shrinkage : float, 0 <= shrinkage <= 1, default 0.1 Coefficient in the convex combination used for the computation of the shrunk estimate. assume_centered : boolean, default False If True, data are not centered

This is an example showing how scikit-learn can be used to classify documents by topics using a bag-of-words approach. This example uses a scipy.sparse matrix to store the features and demonstrates various classifiers that can efficiently handle sparse matrices. The dataset used in this example is the 20 newsgroups dataset. It will be automatically downloaded, then cached. The bar plot indicates the accuracy, training time (normalized) and test time (normalized) of each classifier. # Author: P

sklearn.datasets.load_files(container_path, description=None, categories=None, load_content=True, shuffle=True, encoding=None, decode_error='strict', random_state=0) [source] Load text files with categories as subfolder names. Individual samples are assumed to be files stored a two levels folder structure such as the following: container_folder/ category_1_folder/ file_1.txt file_2.txt ... file_42.txt category_2_folder/ file_43.txt file_44.txt ... The folder names are used as supervised

class sklearn.decomposition.IncrementalPCA(n_components=None, whiten=False, copy=True, batch_size=None) [source] Incremental principal components analysis (IPCA). Linear dimensionality reduction using Singular Value Decomposition of centered data, keeping only the most significant singular vectors to project the data to a lower dimensional space. Depending on the size of the input data, this algorithm can be much more memory efficient than a PCA. This algorithm has constant memory complexit

sklearn.cluster.k_means(X, n_clusters, init='k-means++', precompute_distances='auto', n_init=10, max_iter=300, verbose=False, tol=0.0001, random_state=None, copy_x=True, n_jobs=1, algorithm='auto', return_n_iter=False) [source] K-means clustering algorithm. Read more in the User Guide. Parameters: X : array-like or sparse matrix, shape (n_samples, n_features) The observations to cluster. n_clusters : int The number of clusters to form as well as the number of centroids to generate. max

This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel (DotProduct). On this particular dataset, the DotProduct kernel obtains considerably better results because the class-boundaries are linear and coincide with the coordinate axes. In general, stationary kernels often obtain better results. print(__doc__) # Authors: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de> # # License: BSD 3 clause import numpy as np import

Biclustering can be performed with the module sklearn.cluster.bicluster. Biclustering algorithms simultaneously cluster rows and columns of a data matrix. These clusters of rows and columns are known as biclusters. Each determines a submatrix of the original data matrix with some desired properties. For instance, given a matrix of shape (10, 10), one possible bicluster with three rows and two columns induces a submatrix of shape (3, 2): >>> import numpy as np >>> data = np.ar