This example demonstrates the Spectral Co-clustering algorithm on the twenty newsgroups dataset. The ?comp.os.ms-windows.misc? category is excluded because it contains many posts containing nothing but data. The TF-IDF vectorized posts form a word frequency matrix, which is then biclustered using Dhillon?s Spectral Co-Clustering algorithm. The resulting document-word biclusters indicate subsets words used more often in those subsets documents. For a few of the best biclusters, its most common

class sklearn.neighbors.KDTree KDTree for fast generalized N-point problems KDTree(X, leaf_size=40, metric=?minkowski?, **kwargs) Parameters: X : array-like, shape = [n_samples, n_features] n_samples is the number of points in the data set, and n_features is the dimension of the parameter space. Note: if X is a C-contiguous array of doubles then data will not be copied. Otherwise, an internal copy will be made. leaf_size : positive integer (default = 40) Number of points at which to swi

sklearn.linear_model.orthogonal_mp_gram(Gram, Xy, n_nonzero_coefs=None, tol=None, norms_squared=None, copy_Gram=True, copy_Xy=True, return_path=False, return_n_iter=False) [source] Gram Orthogonal Matching Pursuit (OMP) Solves n_targets Orthogonal Matching Pursuit problems using only the Gram matrix X.T * X and the product X.T * y. Read more in the User Guide. Parameters: Gram : array, shape (n_features, n_features) Gram matrix of the input data: X.T * X Xy : array, shape (n_features,) o

sklearn.datasets.load_digits(n_class=10, return_X_y=False) [source] Load and return the digits dataset (classification). Each datapoint is a 8x8 image of a digit. Classes 10 Samples per class ~180 Samples total 1797 Dimensionality 64 Features integers 0-16 Read more in the User Guide. Parameters: n_class : integer, between 0 and 10, optional (default=10) The number of classes to return. return_X_y : boolean, default=False. If True, returns (data, target) instead of a Bunch object. See b

An application of the different Manifold learning techniques on a spherical data-set. Here one can see the use of dimensionality reduction in order to gain some intuition regarding the manifold learning methods. Regarding the dataset, the poles are cut from the sphere, as well as a thin slice down its side. This enables the manifold learning techniques to ?spread it open? whilst projecting it onto two dimensions. For a similar example, where the methods are applied to the S-curve dataset, see

sklearn.metrics.log_loss(y_true, y_pred, eps=1e-15, normalize=True, sample_weight=None, labels=None) [source] Log loss, aka logistic loss or cross-entropy loss. This is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of the true labels given a probabilistic classifier?s predictions. The log loss is only defined for two or more labels. For a single sample with true label yt in {0,1} and estimated

Warning All classifiers in scikit-learn do multiclass classification out-of-the-box. You don?t need to use the sklearn.multiclass module unless you want to experiment with different multiclass strategies. The sklearn.multiclass module implements meta-estimators to solve multiclass and multilabel classification problems by decomposing such problems into binary classification problems. Multitarget regression is also supported. Multiclass classification means a classification task with more t

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

This example illustrates and compares the bias-variance decomposition of the expected mean squared error of a single estimator against a bagging ensemble. In regression, the expected mean squared error of an estimator can be decomposed in terms of bias, variance and noise. On average over datasets of the regression problem, the bias term measures the average amount by which the predictions of the estimator differ from the predictions of the best possible estimator for the problem (i.e., the Ba