Show below is a logistic-regression classifiers decision boundaries on the iris dataset. The datapoints are colored according to their labels. print(__doc__) # Code source: Ga Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model, datasets # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. Y = iris.target h

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

A recursive feature elimination example showing the relevance of pixels in a digit classification task. Note See also Recursive feature elimination with cross-validation print(__doc__) from sklearn.svm import SVC from sklearn.datasets import load_digits from sklearn.feature_selection import RFE import matplotlib.pyplot as plt # Load the digits dataset digits = load_digits() X = digits.images.reshape((len(digits.images), -1)) y = digits.target # Create the RFE object and rank each pixe

2.9.1. Restricted Boltzmann machines Restricted Boltzmann machines (RBM) are unsupervised nonlinear feature learners based on a probabilistic model. The features extracted by an RBM or a hierarchy of RBMs often give good results when fed into a linear classifier such as a linear SVM or a perceptron. The model makes assumptions regarding the distribution of inputs. At the moment, scikit-learn only provides BernoulliRBM, which assumes the inputs are either binary values or values between 0 and

class sklearn.preprocessing.RobustScaler(with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True) [source] Scale features using statistics that are robust to outliers. This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile). Centering and scaling happen independently on each feature (or each sample, depen

class sklearn.model_selection.StratifiedKFold(n_splits=3, shuffle=False, random_state=None) [source] Stratified K-Folds cross-validator Provides train/test indices to split data in train/test sets. This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class. Read more in the User Guide. Parameters: n_splits : int, default=3 Number of folds. Must be at least 2. shuffle : boolean, optional Wh

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.RBF(length_scale=1.0, length_scale_bounds=(1e-05, 100000.0)) [source] Radial-basis function kernel (aka squared-exponential kernel). The RBF kernel is a stationary kernel. It is also known as the ?squared exponential? kernel. It is parameterized by a length-scale parameter length_scale>0, which can either be a scalar (isotropic variant of the kernel) or a vector with the same number of dimensions as the inputs X (anisotropic variant of the kernel).

This example compares 2 dimensionality reduction strategies: univariate feature selection with Anova feature agglomeration with Ward hierarchical clustering Both methods are compared in a regression problem using a BayesianRidge as supervised estimator. # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr> # License: BSD 3 clause print(__doc__) import shutil import tempfile import numpy as np import matplotlib.pyplot as plt from scipy import linalg, ndimage from sklearn.featur

A recursive feature elimination example with automatic tuning of the number of features selected with cross-validation. Out: Optimal number of features : 3 print(__doc__) import matplotlib.pyplot as plt from sklearn.svm import SVC from sklearn.model_selection import StratifiedKFold from sklearn.feature_selection import RFECV from sklearn.datasets import make_classification # Build a classification task using 3 informative features X, y = make_classification(n_samples=1000, n_features=2