class sklearn.preprocessing.MaxAbsScaler(copy=True) [source] Scale each feature by its maximum absolute value. This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity. This scaler can also be applied to sparse CSR or CSC matrices. New in version 0.17. Parameters: copy : boolean, optional, default is True Set to False to pe

class sklearn.feature_extraction.image.PatchExtractor(patch_size=None, max_patches=None, random_state=None) [source] Extracts patches from a collection of images Read more in the User Guide. Parameters: patch_size : tuple of ints (patch_height, patch_width) the dimensions of one patch max_patches : integer or float, optional default is None The maximum number of patches per image to extract. If max_patches is a float in (0, 1), it is taken to mean a proportion of the total number of pat

Linear Discriminant Analysis (discriminant_analysis.LinearDiscriminantAnalysis) and Quadratic Discriminant Analysis (discriminant_analysis.QuadraticDiscriminantAnalysis) are two classic classifiers, with, as their names suggest, a linear and a quadratic decision surface, respectively. These classifiers are attractive because they have closed-form solutions that can be easily computed, are inherently multiclass, have proven to work well in practice and have no hyperparameters to tune. The plo

sklearn.metrics.silhouette_samples(X, labels, metric='euclidean', **kwds) [source] Compute the Silhouette Coefficient for each sample. The Silhouette Coefficient is a measure of how well samples are clustered with samples that are similar to themselves. Clustering models with a high Silhouette Coefficient are said to be dense, where samples in the same cluster are similar to each other, and well separated, where samples in different clusters are not very similar to each other. The Silhouett

sklearn.neighbors.kneighbors_graph(X, n_neighbors, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False, n_jobs=1) [source] Computes the (weighted) graph of k-Neighbors for points in X Read more in the User Guide. Parameters: X : array-like or BallTree, shape = [n_samples, n_features] Sample data, in the form of a numpy array or a precomputed BallTree. n_neighbors : int Number of neighbors for each sample. mode : {?connectivity?, ?distance?}, optional T

Making sure that each Feature has approximately the same scale can be a crucial preprocessing step. However, when data contains outliers, StandardScaler can often be mislead. In such cases, it is better to use a scaler that is robust against outliers. Here, we demonstrate this on a toy dataset, where one single datapoint is a large outlier. Out: Testset accuracy using standard scaler: 0.545 Testset accuracy using robust scaler: 0.705 from __future__ import print_function print(__doc_

This is an example showing how scikit-learn can be used to classify documents by topics using a bag-of-words approach. This example uses a scipy.sparse matrix to store the features and demonstrates various classifiers that can efficiently handle sparse matrices. The dataset used in this example is the 20 newsgroups dataset. It will be automatically downloaded, then cached. The bar plot indicates the accuracy, training time (normalized) and test time (normalized) of each classifier. # Author: P

When working with covariance estimation, the usual approach is to use a maximum likelihood estimator, such as the sklearn.covariance.EmpiricalCovariance. It is unbiased, i.e. it converges to the true (population) covariance when given many observations. However, it can also be beneficial to regularize it, in order to reduce its variance; this, in turn, introduces some bias. This example illustrates the simple regularization used in Shrunk Covariance estimators. In particular, it focuses on how

sklearn.metrics.pairwise.additive_chi2_kernel(X, Y=None) [source] Computes the additive chi-squared kernel between observations in X and Y The chi-squared kernel is computed between each pair of rows in X and Y. X and Y have to be non-negative. This kernel is most commonly applied to histograms. The chi-squared kernel is given by: k(x, y) = -Sum [(x - y)^2 / (x + y)] It can be interpreted as a weighted difference per entry. Read more in the User Guide. Parameters: X : array-like of shape

class sklearn.decomposition.IncrementalPCA(n_components=None, whiten=False, copy=True, batch_size=None) [source] Incremental principal components analysis (IPCA). Linear dimensionality reduction using Singular Value Decomposition of centered data, keeping only the most significant singular vectors to project the data to a lower dimensional space. Depending on the size of the input data, this algorithm can be much more memory efficient than a PCA. This algorithm has constant memory complexit