![]() ![]() The clusters are then placed on the vertices of the Randomly linearly combined within each cluster in order to addĬovariance. Informative features are drawn independently from N(0, 1) and then In a subspace of dimension n_informative. Of gaussian clusters each located around the vertices of a hypercube N_features-n_informative-n_redundant-n_repeated useless featuresĭrawn at random. Informative features, n_redundant redundant features, Thus, without shuffling, all useful features are contained in the columns ![]() The remaining features are filled with random noise. Linear combinations of the informative features, followed by n_repeatedĭuplicates, drawn randomly with replacement from the informative and Order: the primary n_informative features, followed by n_redundant Without shuffling, X horizontally stacks features in the following Various types of further noise to the data. It introduces interdependence between these features and adds Length 2*class_sep and assigns an equal number of clusters to eachĬlass. This initially creates clusters of points normally distributed (std=1)Ībout vertices of an n_informative-dimensional hypercube with sides of Generate a random n-class classification problem. make_classification ( n_samples = 100, n_features = 20, *, n_informative = 2, n_redundant = 2, n_repeated = 0, n_classes = 2, n_clusters_per_class = 2, weights = None, flip_y = 0.01, class_sep = 1.0, hypercube = True, shift = 0.0, scale = 1.0, shuffle = True, random_state = None ) ¶
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