sklearn.neighbors.radius_neighbors_graph(X, radius, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False, n_jobs=1) [source] Computes the (weighted) graph of Neighbors for points in X Neighborhoods are restricted the points at a distance lower than radius. Read more in the User Guide. Parameters: X : array-like or BallTree, shape = [n_samples, n_features] Sample data, in the form of a numpy array or a precomputed BallTree. radius : float Radius of neighbo

sklearn.neighbors.kneighbors_graph(X, n_neighbors, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False, n_jobs=1) [source] Computes the (weighted) graph of k-Neighbors for points in X Read more in the User Guide. Parameters: X : array-like or BallTree, shape = [n_samples, n_features] Sample data, in the form of a numpy array or a precomputed BallTree. n_neighbors : int Number of neighbors for each sample. mode : {?connectivity?, ?distance?}, optional T

sklearn.model_selection.validation_curve(estimator, X, y, param_name, param_range, groups=None, cv=None, scoring=None, n_jobs=1, pre_dispatch='all', verbose=0) [source] Validation curve. Determine training and test scores for varying parameter values. Compute scores for an estimator with different values of a specified parameter. This is similar to grid search with one parameter. However, this will also compute training scores and is merely a utility for plotting the results. Read more in t

sklearn.model_selection.train_test_split(*arrays, **options) [source] Split arrays or matrices into random train and test subsets Quick utility that wraps input validation and next(ShuffleSplit().split(X, y)) and application to input data into a single call for splitting (and optionally subsampling) data in a oneliner. Read more in the User Guide. Parameters: *arrays : sequence of indexables with same length / shape[0] Allowed inputs are lists, numpy arrays, scipy-sparse matrices or panda

sklearn.model_selection.permutation_test_score(estimator, X, y, groups=None, cv=None, n_permutations=100, n_jobs=1, random_state=0, verbose=0, scoring=None) [source] Evaluate the significance of a cross-validated score with permutations Read more in the User Guide. Parameters: estimator : estimator object implementing ?fit? The object to use to fit the data. X : array-like of shape at least 2D The data to fit. y : array-like The target variable to try to predict in the case of supervi

sklearn.model_selection.learning_curve(estimator, X, y, groups=None, train_sizes=array([ 0.1, 0.33, 0.55, 0.78, 1. ]), cv=None, scoring=None, exploit_incremental_learning=False, n_jobs=1, pre_dispatch='all', verbose=0) [source] Learning curve. Determines cross-validated training and test scores for different training set sizes. A cross-validation generator splits the whole dataset k times in training and test data. Subsets of the training set with varying sizes will be used to train the est

sklearn.model_selection.fit_grid_point(X, y, estimator, parameters, train, test, scorer, verbose, error_score='raise', **fit_params) [source] Run fit on one set of parameters. Parameters: X : array-like, sparse matrix or list Input data. y : array-like or None Targets for input data. estimator : estimator object A object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score functio

sklearn.model_selection.cross_val_score(estimator, X, y=None, groups=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch='2*n_jobs') [source] Evaluate a score by cross-validation Read more in the User Guide. Parameters: estimator : estimator object implementing ?fit? The object to use to fit the data. X : array-like The data to fit. Can be, for example a list, or an array at least 2d. y : array-like, optional, default: None The target variable to try to pre

sklearn.model_selection.cross_val_predict(estimator, X, y=None, groups=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch='2*n_jobs', method='predict') [source] Generate cross-validated estimates for each input data point Read more in the User Guide. Parameters: estimator : estimator object implementing ?fit? and ?predict? The object to use to fit the data. X : array-like The data to fit. Can be, for example a list, or an array at least 2d. y : array-like, optional, defa

sklearn.model_selection.check_cv(cv=3, y=None, classifier=False) [source] Input checker utility for building a cross-validator Parameters: cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: None, to use the default 3-fold cross-validation, integer, to specify the number of folds. An object to be used as a cross-validation generator. An iterable yielding train/test splits. For integer/None inputs, i