The problem solved in supervised learning Supervised learning consists in learning the link between two datasets: the observed data X and an external variable y that we are trying to predict, usually called ?target? or ?labels?. Most often, y is a 1D array of length n_samples. All supervised estimators in scikit-learn implement a fit(X, y) method to fit the model and a predict(X) method that, given unlabeled observations X, returns the predicted labels y. Vocabulary: classification and regr

Datasets Scikit-learn deals with learning information from one or more datasets that are represented as 2D arrays. They can be understood as a list of multi-dimensional observations. We say that the first axis of these arrays is the samples axis, while the second is the features axis. A simple example shipped with the scikit: iris dataset >>> from sklearn import datasets >>> iris = datasets.load_iris() >>> data = iris.data >>> data.shape (150, 4) It is ma

In this example, an image with connected circles is generated and spectral clustering is used to separate the circles. In these settings, the Spectral clustering approach solves the problem know as ?normalized graph cuts?: the image is seen as a graph of connected voxels, and the spectral clustering algorithm amounts to choosing graph cuts defining regions while minimizing the ratio of the gradient along the cut, and the volume of the region. As the algorithm tries to balance the volume (ie ba

Modeling species? geographic distributions is an important problem in conservation biology. In this example we model the geographic distribution of two south american mammals given past observations and 14 environmental variables. Since we have only positive examples (there are no unsuccessful observations), we cast this problem as a density estimation problem and use the OneClassSVM provided by the package sklearn.svm as our modeling tool. The dataset is provided by Phillips et. al. (2006). I

Features 1 and 2 of the diabetes-dataset are fitted and plotted below. It illustrates that although feature 2 has a strong coefficient on the full model, it does not give us much regarding y when compared to just feature 1 print(__doc__) # Code source: Ga Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from mpl_toolkits.mplot3d import Axes3D from sklearn import datasets, linear_model diabetes = datasets.lo

Given a small number of observations, we want to recover which features of X are relevant to explain y. For this sparse linear models can outperform standard statistical tests if the true model is sparse, i.e. if a small fraction of the features are relevant. As detailed in the compressive sensing notes, the ability of L1-based approach to identify the relevant variables depends on the sparsity of the ground truth, the number of samples, the number of features, the conditioning of the design m

Using the GraphLasso estimator to learn a covariance and sparse precision from a small number of samples. To estimate a probabilistic model (e.g. a Gaussian model), estimating the precision matrix, that is the inverse covariance matrix, is as important as estimating the covariance matrix. Indeed a Gaussian model is parametrized by the precision matrix. To be in favorable recovery conditions, we sample the data from a model with a sparse inverse covariance matrix. In addition, we ensure that th

Transform a signal as a sparse combination of Ricker wavelets. This example visually compares different sparse coding methods using the sklearn.decomposition.SparseCoder estimator. The Ricker (also known as Mexican hat or the second derivative of a Gaussian) is not a particularly good kernel to represent piecewise constant signals like this one. It can therefore be seen how much adding different widths of atoms matters and it therefore motivates learning the dictionary to best fit your type of

sklearn.utils.shuffle(*arrays, **options) [source] Shuffle arrays or sparse matrices in a consistent way This is a convenience alias to resample(*arrays, replace=False) to do random permutations of the collections. Parameters: *arrays : sequence of indexable data-structures Indexable data-structures can be arrays, lists, dataframes or scipy sparse matrices with consistent first dimension. random_state : int or RandomState instance Control the shuffling for reproducible behavior. n_samp

sklearn.utils.resample(*arrays, **options) [source] Resample arrays or sparse matrices in a consistent way The default strategy implements one step of the bootstrapping procedure. Parameters: *arrays : sequence of indexable data-structures Indexable data-structures can be arrays, lists, dataframes or scipy sparse matrices with consistent first dimension. replace : boolean, True by default Implements resampling with replacement. If False, this will implement (sliced) random permutations.