class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1) [source] Ordinary least squares Linear Regression. Parameters: fit_intercept : boolean, optional 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). normalize : boolean, optional, default False If True, the regressors X will be normalized before regression. This parameter is ignor

Out-of-bag (OOB) estimates can be a useful heuristic to estimate the ?optimal? number of boosting iterations. OOB estimates are almost identical to cross-validation estimates but they can be computed on-the-fly without the need for repeated model fitting. OOB estimates are only available for Stochastic Gradient Boosting (i.e. subsample < 1.0), the estimates are derived from the improvement in loss based on the examples not included in the bootstrap sample (the so-called out-of-bag examples)

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

class sklearn.gaussian_process.kernels.Hyperparameter [source] A kernel hyperparameter?s specification in form of a namedtuple. New in version 0.18. Attributes: name : string The name of the hyperparameter. Note that a kernel using a hyperparameter with name ?x? must have the attributes self.x and self.x_bounds value_type : string The type of the hyperparameter. Currently, only ?numeric? hyperparameters are supported. bounds : pair of floats >= 0 or ?fixed? The lower and upper bo

sklearn.manifold.locally_linear_embedding(X, n_neighbors, n_components, reg=0.001, eigen_solver='auto', tol=1e-06, max_iter=100, method='standard', hessian_tol=0.0001, modified_tol=1e-12, random_state=None, n_jobs=1) [source] Perform a Locally Linear Embedding analysis on the data. Read more in the User Guide. Parameters: X : {array-like, sparse matrix, BallTree, KDTree, NearestNeighbors} Sample data, shape = (n_samples, n_features), in the form of a numpy array, sparse array, precomputed

sklearn.svm.l1_min_c(X, y, loss='squared_hinge', fit_intercept=True, intercept_scaling=1.0) [source] Return the lowest bound for C such that for C in (l1_min_C, infinity) the model is guaranteed not to be empty. This applies to l1 penalized classifiers, such as LinearSVC with penalty=?l1? and linear_model.LogisticRegression with penalty=?l1?. This value is valid if class_weight parameter in fit() is not set. Parameters: X : array-like or sparse matrix, shape = [n_samples, n_features] Trai

class sklearn.decomposition.TruncatedSVD(n_components=2, algorithm='randomized', n_iter=5, random_state=None, tol=0.0) [source] Dimensionality reduction using truncated SVD (aka LSA). This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). Contrary to PCA, this estimator does not center the data before computing the singular value decomposition. This means it can work with scipy.sparse matrices efficiently. In particular, truncated

This example demonstrates the behaviour of the accuracy of the nearest neighbor queries of Locality Sensitive Hashing Forest as the number of candidates and the number of estimators (trees) vary. In the first plot, accuracy is measured with the number of candidates. Here, the term ?number of candidates? refers to maximum bound for the number of distinct points retrieved from each tree to calculate the distances. Nearest neighbors are selected from this pool of candidates. Number of estimators

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

Warning DEPRECATED class sklearn.cross_validation.LeaveOneOut(n) [source] Leave-One-Out cross validation iterator. Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.LeaveOneOut instead. Provides train/test indices to split data in train test sets. Each sample is used once as a test set (singleton) while the remaining samples form the training set. Note: LeaveOneOut(n) is equivalent to KFold(n, n_folds=n) and LeavePOut(n, p=1). Due to the hig