class sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None) [source] K-Folds cross-validator Provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default). Each fold is then used once as a validation while the k - 1 remaining folds form the training set. Read more in the User Guide. Parameters: n_splits : int, default=3 Number of folds. Must be at least 2. shuffle : boolean, optional Whether to s

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

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

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

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.metrics.pairwise.additive_chi2_kernel(X, Y=None) [source] Computes the additive chi-squared kernel between observations in X and Y The chi-squared kernel is computed between each pair of rows in X and Y. X and Y have to be non-negative. This kernel is most commonly applied to histograms. The chi-squared kernel is given by: k(x, y) = -Sum [(x - y)^2 / (x + y)] It can be interpreted as a weighted difference per entry. Read more in the User Guide. Parameters: X : array-like of shape

When working with covariance estimation, the usual approach is to use a maximum likelihood estimator, such as the sklearn.covariance.EmpiricalCovariance. It is unbiased, i.e. it converges to the true (population) covariance when given many observations. However, it can also be beneficial to regularize it, in order to reduce its variance; this, in turn, introduces some bias. This example illustrates the simple regularization used in Shrunk Covariance estimators. In particular, it focuses on how

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

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

sklearn.ensemble.partial_dependence.partial_dependence(gbrt, target_variables, grid=None, X=None, percentiles=(0.05, 0.95), grid_resolution=100) [source] Partial dependence of target_variables. Partial dependence plots show the dependence between the joint values of the target_variables and the function represented by the gbrt. Read more in the User Guide. Parameters: gbrt : BaseGradientBoosting A fitted gradient boosting model. target_variables : array-like, dtype=int The target featur