# Examples

## General examples

General-purpose and introductory examples for the scikit.

Simple usage of Pipeline that runs successively a univariate feature selection with anova and t...

A recursive feature elimination example showing the relevance of pixels in a digit classificati...

A recursive feature elimination example with automatic tuning of the number of features selecte...

Example of confusion matrix usage to evaluate the quality of the output of a classifier on the ...

Example of Precision-Recall metric to evaluate the quality of the output of a classifier.

In many real-world examples, there are many ways to extract features from a dataset. Often it i...

An illustration of the isotonic regression on generated data. The isotonic regression finds a n...

This example shows that imputing the missing values can give better results than discarding the...

Example of Receiver operating characteristic (ROC) metric to evaluate the quality of the output...

This script shows how to sample points from a Hiden Markov Model (HMM): we use a 4-components w...

An example showing how the scikit-learn can be used to recognize images of hand-written digits.

The PCA does an unsupervised dimensionality reduction, while the logistic regression does the p...

In order to test if a classification score is significative a technique in repeating the classi...

Example of Receiver operating characteristic (ROC) metric to evaluate the quality of the output...

This examples shows how a classifier is optimized by "nested" cross-validation, which is done u...

Illustration of how the performance of an estimator on unseen data (test data) is not the same ...

An example showing univariate feature selection.

Plot the classification probability for different classifiers. We use a 3 class dataset, and we...

Compare randomized search and grid search for optimizing hyperparameters of a random forest. Al...

This example simulates a multi-label document classification problem. The dataset is generated ...

This example shows the use of multi-output estimator to complete images. The goal is to predict...

The dataset used in this example is the 20 newsgroups dataset which will be automatically downl...

Compares FeatureHasher and DictVectorizer by using both to vectorize text documents.

The `Johnson-Lindenstrauss lemma`_ states that any high dimensional dataset can be randomly pr...

This is an example showing how the scikit-learn can be used to classify documents by topics usi...

A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this ...

For greyscale image data where pixel values can be interpreted as degrees of blackness on a whi...

Plot the confidence ellipsoids of each class and decision boundary

This is an example showing how the scikit-learn can be used to cluster documents by topics usin...

An example illustrating the approximation of the feature map of an RBF kernel.

This is an example showing how scikit-learn can be used to classify documents by topics using a...

## Examples based on real world datasets

Applications to real world problems with some medium sized datasets or
interactive user interface.

This is a proof of concept application of Non Negative Matrix Factorization of the term frequen...

This example illustrates the need for robust covariance estimation on a real data set. It is us...

This script shows how to use Gaussian HMM. It uses stock price data, which can be obtained from...

This example shows the reconstruction of an image from a set of parallel projections, acquired ...

The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", ...

Modeling species' geographic distributions is an important problem in conservation biology. In ...

This example employs several unsupervised learning techniques to extract the stock market struc...

A classical way to assert the relative importance of vertices in a graph is to compute the prin...

This is an example showing how scikit-learn can be used for classification using an out-of-core...

A simple graphical frontend for Libsvm mainly intended for didactic purposes. You can create da...

## Biclustering

Examples concerning the `sklearn.cluster.bicluster` package.

This example demonstrates how to generate a dataset and bicluster it using the the Spectral Co-...

This example demonstrates how to generate a checkerboard dataset and bicluster it using the Spe...

This example demonstrates the Spectral Co-clustering algorithm on the twenty newsgroups dataset...

## Clustering

Examples concerning the `sklearn.cluster` package.

Compute the segmentation of a 2D image with Ward hierarchical clustering. The clustering is spa...

These images how similar features are merged together using feature agglomeration.

Reference: Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages Between Data Poin...

This example uses :ref:`spectral_clustering` on a graph created from voxel-to-voxel difference ...

Finds core samples of high density and expands clusters from them.

This example uses a large dataset of faces to learn a set of 20 x 20 images patches that consti...

The classic image processing example, Lena, an 8-bit grayscale bit-depth, 512 x 512 sized image...

Example builds a swiss roll dataset and runs hierarchical clustering on their position.

In this example, an image with connected circles is generated and spectral clustering is used t...

The plots display firstly what a K-means algorithm would yield using three clusters. It is then...

Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace (China), reduci...

This example aims at showing characteristics of different clustering algorithms on datasets tha...

This example compares 2 dimensionality reduction strategies:

Evaluate the ability of k-means initializations strategies to make the algorithm convergence ro...

In this example with compare the various initialization strategies for K-means in terms of runt...

The following plots demonstrate the impact of the number of clusters and number of samples on v...

We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is fa...

## Covariance estimation

Examples concerning the `sklearn.covariance` package.

The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and...

When the amount of contamination is known, this example illustrates two different ways of perfo...

Using the GraphLasso estimator to learn a covariance and sparse precision from a small number o...

When working with covariance estimation, the usual approach is to use a maximum likelihood esti...

An example to show covariance estimation with the Mahalanobis distances on Gaussian distributed...

The usual covariance maximum likelihood estimate is very sensitive to the presence of outliers ...

## Cross decomposition

Examples concerning the `sklearn.cross_decomposition` package.

Simple usage of various cross decomposition algorithms: - PLSCanonical - PLSRegression, with mu...

## Dataset examples

Examples concerning the `sklearn.datasets` package.

This dataset is made up of 1797 8x8 images. Each image, like the one shown below, is of a hand-...

Plot several randomly generated 2D classification datasets. This example illustrates the `datas...

The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and P...

## Decomposition

Examples concerning the `sklearn.decomposition` package.

The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 a...

An example of estimating sources from noisy data.

Principal Component Analysis applied to the Iris dataset.

This example shows that Kernel PCA is able to find a projection of the data that makes data lin...

This example illustrates visually in the feature space a comparison by results using two differ...

Transform a signal as a sparse combination of Ricker wavelets. This example visually compares d...

These figures aid in illustrating how a point cloud can be very flat in one direction--which is...

This example applies to :ref:`olivetti_faces` different unsupervised matrix decomposition (dime...

An example comparing the effect of reconstructing noisy fragments of the Lena image using first...

## Ensemble methods

Examples concerning the `sklearn.ensemble` package.

A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D sinusoidal dataset with ...

This example shows the use of forests of trees to evaluate the importance of the pixels in an i...

This examples shows the use of forests of trees to evaluate the importance of features on an ar...

Illustration of the effect of different regularization strategies for Gradient Boosting. The ex...

Demonstrate Gradient Boosting on the boston housing dataset.

Partial dependence plots show the dependence between the target function [1]_ and a set of 'tar...

This example shows how quantile regression can be used to create prediction intervals.

This example fits an AdaBoosted decision stump on a non-linearly separable classification datas...

RandomTreesEmbedding provides a way to map data to a very high-dimensional, sparse representati...

This example reproduces Figure 1 of Zhu et al [1] and shows how boosting can improve prediction...

This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates the difference ...

Out-of-bag (OOB) estimates can be a useful heuristic to estimate the "optimal" number of boosti...

Plot the decision surfaces of forests of randomized trees trained on pairs of features of the i...

## Tutorial exercises

Exercises for the tutorials

A tutorial exercise regarding the use of classification techniques on the Digits dataset.

A tutorial excercise using Cross-validation with an SVM on the Digits dataset.

A tutorial exercise for using different SVM kernels.

A tutorial excercise which uses cross-validation with linear models.

## Gaussian Process for Machine Learning

Examples concerning the `sklearn.gaussian_process` package.

This example consists in fitting a Gaussian Process model onto the diabetes dataset.

A two-dimensional regression exercise with a post-processing allowing for probabilistic classif...

A simple one-dimensional regression exercise computed in two different ways:

## Generalized Linear Models

Examples concerning the `sklearn.linear_model` package.

Computes Lasso Path along the regularization parameter using the LARS algorithm on the diabetes...

Plot the maximum margin separating hyperplane within a two-class separable dataset using a line...

This example demonstrates how to approximate a function with a polynomial of degree n_degree by...

Plot decision function of a weighted dataset, where the size of points is proportional to its w...

Shows the effect of collinearity in the coefficients of an estimator.

An example showing how different online solvers perform on the hand-written digits dataset.

An example that compares various convex loss functions.

Show below is a logistic-regression classifiers decision boundaries on the `iris

Computes path on IRIS dataset.

Fit linear SVMs with and without class weighting. Allows to handle problems with unbalanced cla...

Ridge regression is basically minimizing a penalised version of the least-squared function. The...

The coefficients, the residual sum of squares and the variance score are also calculated.

Show in the plot is how the logistic regression would, in this synthetic dataset, classify valu...

The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected ...

Plot the contours of the three penalties.

We show that linear_model.Lasso provides the same results for dense and sparse data and that in...

Estimates Lasso and Elastic-Net regression models on a manually generated sparse signal corrupt...

Computes a Bayesian Ridge Regression on a synthetic dataset.

Features 1 and 2 of the diabetes-dataset are fitted and plotted below. It illustrates that alth...

Fit regression model with Bayesian Ridge Regression.

Comparison of the sparsity (percentage of zero coefficients) of solutions when L1 and L2 penalt...

Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the ...

Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encod...

Lasso and elastic net (L1 and L2 penalisation) implemented using a coordinate descent.

Use the Akaike information criterion (AIC), the Bayes Information criterion (BIC) and cross-val...

Given a small number of observations, we want to recover which features of X are relevant to ex...

## Manifold learning

Examples concerning the `sklearn.manifold` package.

An illustration of Swiss Roll reduction with locally linear embedding

An illustration of the metric and non-metric MDS on generated noisy data.

An illustration of dimensionality reduction on the S-curve dataset with various manifold learni...

An application of the different :ref:`manifold` techniques on a spherical data-set. Here one ca...

An illustration of various embeddings on the digits dataset.

## Gaussian Mixture Models

Examples concerning the `sklearn.mixture` package.

Plot the density estimation of a mixture of two gaussians. Data is generated from two gaussians...

Plot the confidence ellipsoids of a mixture of two gaussians with EM and variational dirichlet ...

This example highlights the advantages of the Dirichlet Process: complexity control and dealing...

This example shows that model selection can be performed with Gaussian Mixture Models using inf...

Demonstration of Gaussian mixture models for classification.

## Nearest Neighbors

Examples concerning the `sklearn.neighbors` package.

Demonstrate the resolution of a regression problem using a k-Nearest Neighbor and the interpola...

Sample usage of Nearest Neighbors classification. It will plot the decision boundaries for each...

Sample usage of Nearest Centroid classification. It will plot the decision boundaries for each ...

This example shows how kernel density estimation (KDE), a powerful non-parametric density estim...

This example does not perform any learning over the data (see :ref:`example_applications_plot_s...

The first plot shows one of the problems with using histograms to visualize the density of poin...

## Semi Supervised Classification

Examples concerning the `sklearn.semi_supervised` package.

Example of LabelPropagation learning a complex internal structure to demonstrate "manifold lear...

Comparison for decision boundary generated on iris dataset between Label Propagation and SVM.

This example demonstrates the power of semisupervised learning by training a Label Spreading mo...

Demonstrates an active learning technique to learn handwritten digits using label propagation.

## Support Vector Machines

Examples concerning the `sklearn.svm` package.

Perform binary classification using non-linear SVC with RBF kernel. The target to predict is a ...

Toy example of 1D regression using linear, polynominial and RBF kernels.

Plot the maximum margin separating hyperplane within a two-class separable dataset using a Supp...

Find the optimal separating hyperplane using an SVC for classes that are unbalanced.

The decision boundaries, are shown with all the points in the training-set.

Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface...

This example shows how to perform univariate feature before running a SVC (support vector class...

Plot decision function of a weighted dataset, where the size of points is proportional to its w...

Comparison of different linear SVM classifiers on the iris dataset. It will plot the decision s...

An example using a one-class SVM for novelty detection.

Three different types of SVM-Kernels are displayed below. The polynomial and RBF are especially...

A small value of `C` includes more/all the observations, allowing the margins to be calculated ...

The following example illustrates the effect of scaling the regularization parameter when using...

This example illustrates the effect of the parameters `gamma` and `C` of the rbf kernel SVM.

## Decision Trees

Examples concerning the `sklearn.tree` package.

A 1D regression with decision tree.

An example to illustrate multi-output regression with decision tree.

Plot the decision surface of a decision tree trained on pairs of features of the iris dataset.