sklearn.metrics.brier_score_loss(y_true, y_prob, sample_weight=None, pos_label=None) [source] Compute the Brier score. The smaller the Brier score, the better, hence the naming with ?loss?. Across all items in a set N predictions, the Brier score measures the mean squared difference between (1) the predicted probability assigned to the possible outcomes for item i, and (2) the actual outcome. Therefore, the lower the Brier score is for a set of predictions, the better the predictions are ca

class sklearn.model_selection.GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise', return_train_score=True) [source] Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a ?fit? and a ?score? method. It also implements ?predict?, ?predict_proba?, ?decision_function?, ?transform? and ?inverse_transform? if t

sklearn.metrics.pairwise.euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False, X_norm_squared=None) [source] Considering the rows of X (and Y=X) as vectors, compute the distance matrix between each pair of vectors. For efficiency reasons, the euclidean distance between a pair of row vector x and y is computed as: dist(x, y) = sqrt(dot(x, x) - 2 * dot(x, y) + dot(y, y)) This formulation has two advantages over other ways of computing distances. First, it is computationally effi

class sklearn.linear_model.LarsCV(fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None, max_n_alphas=1000, n_jobs=1, eps=2.2204460492503131e-16, copy_X=True, positive=False) [source] Cross-validated Least Angle Regression model Read more in the User Guide. Parameters: fit_intercept : boolean whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). po

class sklearn.linear_model.OrthogonalMatchingPursuit(n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize=True, precompute='auto') [source] Orthogonal Matching Pursuit model (OMP) Parameters: n_nonzero_coefs : int, optional Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features. tol : float, optional Maximum norm of the residual. If not None, overrides n_nonzero_coefs. fit_intercept : boolean, optional whether to calcul

This example illustrates that GPR with a sum-kernel including a WhiteKernel can estimate the noise level of data. An illustration of the log-marginal-likelihood (LML) landscape shows that there exist two local maxima of LML. The first corresponds to a model with a high noise level and a large length scale, which explains all variations in the data by noise. The second one has a smaller noise level and shorter length scale, which explains most of the variation by the noise-free functional relat

class sklearn.cluster.MeanShift(bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, n_jobs=1) [source] Mean shift clustering using a flat kernel. Mean shift clustering aims to discover ?blobs? in a smooth density of samples. It is a centroid-based algorithm, which works by updating candidates for centroids to be the mean of the points within a given region. These candidates are then filtered in a post-processing stage to eliminate near-duplicates to form the fin

Score, and cross-validated scores As we have seen, every estimator exposes a score method that can judge the quality of the fit (or the prediction) on new data. Bigger is better. >>> from sklearn import datasets, svm >>> digits = datasets.load_digits() >>> X_digits = digits.data >>> y_digits = digits.target >>> svc = svm.SVC(C=1, kernel='linear') >>> svc.fit(X_digits[:-100], y_digits[:-100]).score(X_digits[-100:], y_digits[-100:]) 0.979999

An example using IsolationForest for anomaly detection. The IsolationForest ?isolates? observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. Since recursive partitioning can be represented by a tree structure, the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node. This path length, averaged over a forest of such random tree

sklearn.metrics.jaccard_similarity_score(y_true, y_pred, normalize=True, sample_weight=None) [source] Jaccard similarity coefficient score The Jaccard index [1], or Jaccard similarity coefficient, defined as the size of the intersection divided by the size of the union of two label sets, is used to compare set of predicted labels for a sample to the corresponding set of labels in y_true. Read more in the User Guide. Parameters: y_true : 1d array-like, or label indicator array / sparse matr