sklearn.preprocessing.scale(X, axis=0, with_mean=True, with_std=True, copy=True) [source] Standardize a dataset along any axis Center to the mean and component wise scale to unit variance. Read more in the User Guide. Parameters: X : {array-like, sparse matrix} The data to center and scale. axis : int (0 by default) axis used to compute the means and standard deviations along. If 0, independently standardize each feature, otherwise (if 1) standardize each sample. with_mean : boolean, T

sklearn.preprocessing.robust_scale(X, axis=0, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True) [source] Standardize a dataset along any axis Center to the median and component wise scale according to the interquartile range. Read more in the User Guide. Parameters: X : array-like The data to center and scale. axis : int (0 by default) axis used to compute the medians and IQR along. If 0, independently scale each feature, otherwise (if 1) scale each sample.

sklearn.preprocessing.normalize(X, norm='l2', axis=1, copy=True, return_norm=False) [source] Scale input vectors individually to unit norm (vector length). Read more in the User Guide. Parameters: X : {array-like, sparse matrix}, shape [n_samples, n_features] The data to normalize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy. norm : ?l1?, ?l2?, or ?max?, optional (?l2? by default) The norm to use to normalize each non zero sample (or e

sklearn.preprocessing.minmax_scale(X, feature_range=(0, 1), axis=0, copy=True) [source] Transforms features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, i.e. between zero and one. The transformation is given by: X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) X_scaled = X_std * (max - min) + min where min, max = feature_range. This transformation is often used a

sklearn.preprocessing.maxabs_scale(X, axis=0, copy=True) [source] Scale each feature to the [-1, 1] range without breaking the sparsity. This estimator scales each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. This scaler can also be applied to sparse CSR or CSC matrices. Parameters: axis : int (0 by default) axis used to scale along. If 0, independently scale each feature, otherwise (if 1) scale each sample. copy : boolean, op

sklearn.preprocessing.label_binarize(y, classes, neg_label=0, pos_label=1, sparse_output=False) [source] Binarize labels in a one-vs-all fashion Several regression and binary classification algorithms are available in the scikit. A simple way to extend these algorithms to the multi-class classification case is to use the so-called one-vs-all scheme. This function makes it possible to compute this transformation for a fixed set of class labels known ahead of time. Parameters: y : array-like

sklearn.preprocessing.binarize(X, threshold=0.0, copy=True) [source] Boolean thresholding of array-like or scipy.sparse matrix Read more in the User Guide. Parameters: X : {array-like, sparse matrix}, shape [n_samples, n_features] The data to binarize, element by element. scipy.sparse matrices should be in CSR or CSC format to avoid an un-necessary copy. threshold : float, optional (0.0 by default) Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not

sklearn.preprocessing.add_dummy_feature(X, value=1.0) [source] Augment dataset with an additional dummy feature. This is useful for fitting an intercept term with implementations which cannot otherwise fit it directly. Parameters: X : {array-like, sparse matrix}, shape [n_samples, n_features] Data. value : float Value to use for the dummy feature. Returns: X : {array, sparse matrix}, shape [n_samples, n_features + 1] Same data with dummy feature added as first column. Examples >

sklearn.pipeline.make_union(*transformers) [source] Construct a FeatureUnion from the given transformers. This is a shorthand for the FeatureUnion constructor; it does not require, and does not permit, naming the transformers. Instead, they will be given names automatically based on their types. It also does not allow weighting. Returns: f : FeatureUnion Examples >>> from sklearn.decomposition import PCA, TruncatedSVD >>> make_union(PCA(), TruncatedSVD()) FeatureUnion

sklearn.pipeline.make_pipeline(*steps) [source] Construct a Pipeline from the given estimators. This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically. Returns: p : Pipeline Examples >>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.preprocessing import StandardScaler >>> make_pipeline(StandardScaler(), GaussianN