class sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(100, ), activation='relu', solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08) [source] Multi-layer Perceptron regressor. This model optimizes the square

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

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

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

A comparison of different values for regularization parameter ?alpha? on synthetic datasets. The plot shows that different alphas yield different decision functions. Alpha is a parameter for regularization term, aka penalty term, that combats overfitting by constraining the size of the weights. Increasing alpha may fix high variance (a sign of overfitting) by encouraging smaller weights, resulting in a decision boundary plot that appears with lesser curvatures. Similarly, decreasing alpha may

class sklearn.feature_selection.SelectFpr(score_func=, alpha=0.05) [source] Filter: Select the pvalues below alpha based on a FPR test. FPR test stands for False Positive Rate test. It controls the total amount of false detections. Read more in the User Guide. Parameters: score_func : callable Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues). Default is f_classif (see below ?See also?). The default function only works with classification tasks. alpha :

Warning DEPRECATED class sklearn.cross_validation.LeavePLabelOut(labels, p) [source] Leave-P-Label_Out cross-validation iterator Deprecated since version 0.18: This module will be removed in 0.20. Use sklearn.model_selection.LeavePGroupsOut instead. Provides train/test indices to split data according to a third-party provided label. This label information can be used to encode arbitrary domain specific stratifications of the samples as integers. For instance the labels could be the year

An example showing how the scikit-learn can be used to recognize images of hand-written digits. This example is commented in the tutorial section of the user manual. Out: Classification report for classifier SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False): precision recall f1-score support

Silhouette analysis can be used to study the separation distance between the resulting clusters. The silhouette plot displays a measure of how close each point in one cluster is to points in the neighboring clusters and thus provides a way to assess parameters like number of clusters visually. This measure has a range of [-1, 1]. Silhouette coefficients (as these values are referred to as) near +1 indicate that the sample is far away from the neighboring clusters. A value of 0 indicates that t

Use the Akaike information criterion (AIC), the Bayes Information criterion (BIC) and cross-validation to select an optimal value of the regularization parameter alpha of the Lasso estimator. Results obtained with LassoLarsIC are based on AIC/BIC criteria. Information-criterion based model selection is very fast, but it relies on a proper estimation of degrees of freedom, are derived for large samples (asymptotic results) and assume the model is correct, i.e. that the data are actually generat