This example illustrates how sigmoid calibration changes predicted probabilities for a 3-class classification problem. Illustrated is the standard 2-simplex, where the three corners correspond to the three classes. Arrows point from the probability vectors predicted by an uncalibrated classifier to the probability vectors predicted by the same classifier after sigmoid calibration on a hold-out validation set. Colors indicate the true class of an instance (red: class 1, green: class 2, blue: cl

When performing classification one often wants to predict not only the class label, but also the associated probability. This probability gives some kind of confidence on the prediction. This example demonstrates how to display how well calibrated the predicted probabilities are and how to calibrate an uncalibrated classifier. The experiment is performed on an artificial dataset for binary classification with 100.000 samples (1.000 of them are used for model fitting) with 20 features. Of the 2

This example illustrates the predicted probability of GPC for an RBF kernel with different choices of the hyperparameters. The first figure shows the predicted probability of GPC with arbitrarily chosen hyperparameters and with the hyperparameters corresponding to the maximum log-marginal-likelihood (LML). While the hyperparameters chosen by optimizing LML have a considerable larger LML, they perform slightly worse according to the log-loss on test data. The figure shows that this is because t

These figures aid in illustrating how a point cloud can be very flat in one direction?which is where PCA comes in to choose a direction that is not flat. print(__doc__) # Authors: Gael Varoquaux # Jaques Grobler # Kevin Hughes # License: BSD 3 clause from sklearn.decomposition import PCA from mpl_toolkits.mplot3d import Axes3D import numpy as np import matplotlib.pyplot as plt from scipy import stats Create the data e = np.exp(1) np.random.seed(4) def pdf(x): return

class sklearn.preprocessing.StandardScaler(copy=True, with_mean=True, with_std=True) [source] Standardize features by removing the mean and scaling to unit variance Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation are then stored to be used on later data using the transform method. Standardization of a dataset is a common requirement for many machine learning estimators: they might

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.preprocessing.PolynomialFeatures(degree=2, interaction_only=False, include_bias=True) [source] Generate polynomial and interaction features. Generate a new feature matrix consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, if an input sample is two dimensional and of the form [a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2]. Parameters: degree : integer The degree of the polynomial

class sklearn.preprocessing.OneHotEncoder(n_values='auto', categorical_features='all', dtype=, sparse=True, handle_unknown='error') [source] Encode categorical integer features using a one-hot aka one-of-K scheme. The input to this transformer should be a matrix of integers, denoting the values taken on by categorical (discrete) features. The output will be a sparse matrix where each column corresponds to one possible value of one feature. It is assumed that input features take on values in

class sklearn.preprocessing.Normalizer(norm='l2', copy=True) [source] Normalize samples individually to unit norm. Each sample (i.e. each row of the data matrix) with at least one non zero component is rescaled independently of other samples so that its norm (l1 or l2) equals one. This transformer is able to work both with dense numpy arrays and scipy.sparse matrix (use CSR format if you want to avoid the burden of a copy / conversion). Scaling inputs to unit norms is a common operation for

class sklearn.preprocessing.MultiLabelBinarizer(classes=None, sparse_output=False) [source] Transform between iterable of iterables and a multilabel format Although a list of sets or tuples is a very intuitive format for multilabel data, it is unwieldy to process. This transformer converts between this intuitive format and the supported multilabel format: a (samples x classes) binary matrix indicating the presence of a class label. Parameters: classes : array-like of shape [n_classes] (opt