sklearn.metrics.f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None) [source] Compute the F1 score, also known as balanced F-score or F-measure The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is: F1 = 2 * (precision * recall) / (precision + recall)

Simple usage of Pipeline that runs successively a univariate feature selection with anova and then a C-SVM of the selected features. print(__doc__) from sklearn import svm from sklearn.datasets import samples_generator from sklearn.feature_selection import SelectKBest, f_regression from sklearn.pipeline import make_pipeline # import some data to play with X, y = samples_generator.make_classification( n_features=20, n_informative=3, n_redundant=0, n_classes=4, n_clusters_per_class=2)

sklearn.metrics.consensus_score(a, b, similarity='jaccard') [source] The similarity of two sets of biclusters. Similarity between individual biclusters is computed. Then the best matching between sets is found using the Hungarian algorithm. The final score is the sum of similarities divided by the size of the larger set. Read more in the User Guide. Parameters: a : (rows, columns) Tuple of row and column indicators for a set of biclusters. b : (rows, columns) Another set of biclusters l

sklearn.metrics.pairwise.manhattan_distances(X, Y=None, sum_over_features=True, size_threshold=500000000.0) [source] Compute the L1 distances between the vectors in X and Y. With sum_over_features equal to False it returns the componentwise distances. Read more in the User Guide. Parameters: X : array_like An array with shape (n_samples_X, n_features). Y : array_like, optional An array with shape (n_samples_Y, n_features). sum_over_features : bool, default=True If True the function re

Warning DEPRECATED sklearn.cross_validation.check_cv(cv, X=None, y=None, classifier=False) [source] Input checker utility for building a CV in a user friendly way. Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.check_cv instead. 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

class sklearn.exceptions.NotFittedError [source] Exception class to raise if estimator is used before fitting. This class inherits from both ValueError and AttributeError to help with exception handling and backward compatibility. Examples >>> from sklearn.svm import LinearSVC >>> from sklearn.exceptions import NotFittedError >>> try: ... LinearSVC().predict([[1, 2], [2, 3], [3, 4]]) ... except NotFittedError as e: ... print(repr(e)) ...

sklearn.metrics.pairwise.paired_manhattan_distances(X, Y) [source] Compute the L1 distances between the vectors in X and Y. Read more in the User Guide. Parameters: X : array-like, shape (n_samples, n_features) Y : array-like, shape (n_samples, n_features) Returns: distances : ndarray (n_samples, )

class sklearn.gaussian_process.kernels.Product(k1, k2) [source] Product-kernel k1 * k2 of two kernels k1 and k2. The resulting kernel is defined as k_prod(X, Y) = k1(X, Y) * k2(X, Y) New in version 0.18. Parameters: k1 : Kernel object The first base-kernel of the product-kernel k2 : Kernel object The second base-kernel of the product-kernel Methods clone_with_theta(theta) Returns a clone of self with given hyperparameters theta. diag(X) Returns the diagonal of the kernel k(X, X).

sklearn.metrics.pairwise.paired_distances(X, Y, metric='euclidean', **kwds) [source] Computes the paired distances between X and Y. Computes the distances between (X[0], Y[0]), (X[1], Y[1]), etc... Read more in the User Guide. Parameters: X : ndarray (n_samples, n_features) Array 1 for distance computation. Y : ndarray (n_samples, n_features) Array 2 for distance computation. metric : string or callable The metric to use when calculating distance between instances in a feature array.

Plot the maximum margin separating hyperplane within a two-class separable dataset using a Support Vector Machine classifier with linear kernel. print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm # we create 40 separable points np.random.seed(0) X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]] Y = [0] * 20 + [1] * 20 # fit the model clf = svm.SVC(kernel='linear') clf.fit(X, Y) # get the separating hyperplane w = clf.coef