This example shows that imputing the missing values can give better results than discarding the samples containing any missing value. Imputing does not always improve the predictions, so please check via cross-validation. Sometimes dropping rows or using marker values is more effective. Missing values can be replaced by the mean, the median or the most frequent value using the strategy hyper-parameter. The median is a more robust estimator for data with high magnitude variables which could dom

sklearn.covariance.graph_lasso(emp_cov, alpha, cov_init=None, mode='cd', tol=0.0001, enet_tol=0.0001, max_iter=100, verbose=False, return_costs=False, eps=2.2204460492503131e-16, return_n_iter=False) [source] l1-penalized covariance estimator Read more in the User Guide. Parameters: emp_cov : 2D ndarray, shape (n_features, n_features) Empirical covariance from which to compute the covariance estimate. alpha : positive float The regularization parameter: the higher alpha, the more regula

Plot decision surface of multinomial and One-vs-Rest Logistic Regression. The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers are represented by the dashed lines. Out: training score : 0.995 (multinomial) training score : 0.976 (ovr) print(__doc__) # Authors: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org> # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import make_blobs from sklearn.linear_model import Logist

sklearn.metrics.normalized_mutual_info_score(labels_true, labels_pred) [source] Normalized Mutual Information between two clusterings. Normalized Mutual Information (NMI) is an normalization of the Mutual Information (MI) score to scale the results between 0 (no mutual information) and 1 (perfect correlation). In this function, mutual information is normalized by sqrt(H(labels_true) * H(labels_pred)) This measure is not adjusted for chance. Therefore adjusted_mustual_info_score might be pre

class sklearn.gaussian_process.kernels.Matern(length_scale=1.0, length_scale_bounds=(1e-05, 100000.0), nu=1.5) [source] Matern kernel. The class of Matern kernels is a generalization of the RBF and the absolute exponential kernel parameterized by an additional parameter nu. The smaller nu, the less smooth the approximated function is. For nu=inf, the kernel becomes equivalent to the RBF kernel and for nu=0.5 to the absolute exponential kernel. Important intermediate values are nu=1.5 (once

Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems. The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data. Click on any estimator in the chart below to see its documentation.

sklearn.metrics.get_scorer(scoring) [source]

This example fits an AdaBoosted decision stump on a non-linearly separable classification dataset composed of two ?Gaussian quantiles? clusters (see sklearn.datasets.make_gaussian_quantiles) and plots the decision boundary and decision scores. The distributions of decision scores are shown separately for samples of class A and B. The predicted class label for each sample is determined by the sign of the decision score. Samples with decision scores greater than zero are classified as B, and are

sklearn.model_selection.cross_val_predict(estimator, X, y=None, groups=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch='2*n_jobs', method='predict') [source] Generate cross-validated estimates for each input data point Read more in the User Guide. Parameters: estimator : estimator object implementing ?fit? and ?predict? 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, defa

For greyscale image data where pixel values can be interpreted as degrees of blackness on a white background, like handwritten digit recognition, the Bernoulli Restricted Boltzmann machine model (BernoulliRBM) can perform effective non-linear feature extraction. In order to learn good latent representations from a small dataset, we artificially generate more labeled data by perturbing the training data with linear shifts of 1 pixel in each direction. This example shows how to build a classific