Examples

General examples

General-purpose and introductory examples for the scikit.

Pipeline Anova SVM

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

Recursive feature elimination

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

Recursive feature elimination with cross-validation

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

Confusion matrix

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

Plotting Validation Curves

In this plot you can see the training scores and validation scores of an SVM for different valu...

Underfitting vs. Overfitting

This example demonstrates the problems of underfitting and overfitting and how we can use linea...

Concatenating multiple feature extraction methods

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

Isotonic Regression

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

Imputing missing values before building an estimator

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

Pipelining: chaining a PCA and a logistic regression

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

Recognizing hand-written digits

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

Test with permutations the significance of a classification score

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

Parameter estimation using grid search with cross-validation

This examples shows how a classifier is optimized by cross-validation, which is done using the ...

Receiver Operating Characteristic (ROC) with cross validation

Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality...

Train error vs Test error

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

Univariate Feature Selection

An example showing univariate feature selection.

Plot classification probability

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

Comparing randomized search and grid search for hyperparameter estimation

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

Receiver Operating Characteristic (ROC)

Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality...

Precision-Recall

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

Multilabel classification

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

Face completion with a multi-output estimators

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

Sample pipeline for text feature extraction and evaluation

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

Plotting Learning Curves

On the left side the learning curve of a naive Bayes classifier is shown for the digits dataset...

FeatureHasher and DictVectorizer Comparison

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

The Johnson-Lindenstrauss bound for embedding with random projections

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

Classifier comparison

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

Restricted Boltzmann Machine features for digit classification

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

Linear and Quadratic Discriminant Analysis with confidence ellipsoid

Plot the confidence ellipsoids of each class and decision boundary

Clustering text documents using k-means

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

Explicit feature map approximation for RBF kernels

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

Classification of text documents using sparse features

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.

Topics extraction with Non-Negative Matrix Factorization

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

Outlier detection on a real data set

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

Compressive sensing: tomography reconstruction with L1 prior (Lasso)

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

Faces recognition example using eigenfaces and SVMs

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

Model Complexity Influence

Demonstrate how model complexity influences both prediction accuracy and computational performa...

Species distribution modeling

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

Visualizing the stock market structure

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

Wikipedia principal eigenvector

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

Prediction Latency

This is an example showing the prediction latency of various scikit-learn estimators.

Libsvm GUI

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

Out-of-core classification of text documents

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

Biclustering

Examples concerning the sklearn.cluster.bicluster package.

A demo of the Spectral Co-Clustering algorithm

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

A demo of the Spectral Biclustering algorithm

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

Biclustering documents with the Spectral Co-clustering algorithm

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

Clustering

Examples concerning the sklearn.cluster package.

A demo of the mean-shift clustering algorithm

Reference:

A demo of structured Ward hierarchical clustering on Lena image

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

Feature agglomeration

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

Demo of affinity propagation clustering algorithm

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

Agglomerative clustering with and without structure

This example shows the effect of imposing a connectivity graph to capture local structure in th...

Segmenting the picture of Lena in regions

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

Demo of DBSCAN clustering algorithm

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

Online learning of a dictionary of parts of faces

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

Vector Quantization Example

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

Hierarchical clustering: structured vs unstructured ward

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

Spectral clustering for image segmentation

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

K-means Clustering

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

Various Agglomerative Clustering on a 2D embedding of digits

An illustration of various linkage option for agglomerative clustering on a 2D embedding of the...

Color Quantization using K-Means

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

Feature agglomeration vs. univariate selection

This example compares 2 dimensionality reduction strategies:

Agglomerative clustering with different metrics

Demonstrates the effect of different metrics on the hierarchical clustering.

Empirical evaluation of the impact of k-means initialization

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

A demo of K-Means clustering on the handwritten digits data

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

Adjustment for chance in clustering performance evaluation

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

Comparing different clustering algorithms on toy datasets

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

Comparison of the K-Means and MiniBatchKMeans clustering algorithms

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

Covariance estimation

Examples concerning the sklearn.covariance package.

Ledoit-Wolf vs OAS estimation

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

Outlier detection with several methods.

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

Sparse inverse covariance estimation

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

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood

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

Robust covariance estimation and Mahalanobis distances relevance

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

Robust vs Empirical covariance estimate

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

Cross decomposition

Examples concerning the sklearn.cross_decomposition package.

Compare cross decomposition methods

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

Dataset examples

Examples concerning the sklearn.datasets package.

The Digit Dataset

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

Plot randomly generated classification dataset

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

The Iris Dataset

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

Decomposition

Examples concerning the sklearn.decomposition package.

Comparison of LDA and PCA 2D projection of Iris dataset

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

PCA example with Iris Data-set

Principal Component Analysis applied to the Iris dataset.

Blind source separation using FastICA

An example of estimating sources from noisy data.

Kernel PCA

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

FastICA on 2D point clouds

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

Sparse coding with a precomputed dictionary

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

Principal components analysis (PCA)

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

Model selection with Probabilistic (PCA) and Factor Analysis (FA)

Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the lik...

Faces dataset decompositions

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

Image denoising using dictionary learning

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

Ensemble methods

Examples concerning the sklearn.ensemble package.

Pixel importances with a parallel forest of trees

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

Decision Tree Regression with AdaBoost

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

Feature importances with forests of trees

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

Gradient Boosting regularization

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

Partial Dependence Plots

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

Gradient Boosting regression

Demonstrate Gradient Boosting on the boston housing dataset.

Prediction Intervals for Gradient Boosting Regression

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

Two-class AdaBoost

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

Hashing feature transformation using Totally Random Trees

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

Discrete versus Real AdaBoost

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

Multi-class AdaBoosted Decision Trees

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

Gradient Boosting Out-of-Bag estimates

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

Plot the decision surfaces of ensembles of trees on the iris dataset

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

Single estimator versus bagging: bias-variance decomposition

This example illustrates and compares the bias-variance decomposition of the expected mean squa...

Tutorial exercises

Exercises for the tutorials

Digits Classification Exercise

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

Cross-validation on Digits Dataset Exercise

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

SVM Exercise

A tutorial exercise for using different SVM kernels.

Cross-validation on diabetes Dataset Exercise

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

Gaussian Process for Machine Learning

Examples concerning the sklearn.gaussian_process package.

Gaussian Processes regression: goodness-of-fit on the ‘diabetes’ dataset

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

Gaussian Processes classification example: exploiting the probabilistic output

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

Gaussian Processes regression: basic introductory example

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

Generalized Linear Models

Examples concerning the sklearn.linear_model package.

Lasso path using LARS

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

SGD: convex loss functions

A plot that compares the various convex loss functions supported by :class:`sklearn.linear_mode...

SGD: Maximum margin separating hyperplane

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

SGD: Weighted samples

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

Plot Ridge coefficients as a function of the regularization

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

Comparing various online solvers

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

Robust linear model estimation using RANSAC

In this example we see how to robustly fit a linear model to faulty data using the RANSAC algor...

Polynomial interpolation

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

Logistic Regression 3-class Classifier

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

Path with L1- Logistic Regression

Computes path on IRIS dataset.

Ordinary Least Squares and Ridge Regression Variance

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

Linear Regression Example

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

Logit function

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

Joint feature selection with multi-task Lasso

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

SGD: Penalties

Plot the contours of the three penalties.

Lasso on dense and sparse data

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

Lasso and Elastic Net for Sparse Signals

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

Bayesian Ridge Regression

Computes a Bayesian Ridge Regression on a synthetic dataset.

Sparsity Example: Fitting only features 1 and 2

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

Automatic Relevance Determination Regression (ARD)

Fit regression model with Bayesian Ridge Regression.

L1 Penalty and Sparsity in Logistic Regression

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

Plot multi-class SGD on the iris dataset

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

Orthogonal Matching Pursuit

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

Lasso and Elastic Net

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

Lasso model selection: Cross-Validation / AIC / BIC

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

Sparse recovery: feature selection for sparse linear models

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.

Swiss Roll reduction with LLE

An illustration of Swiss Roll reduction with locally linear embedding

Multi-dimensional scaling

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

Comparison of Manifold Learning methods

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

Manifold Learning methods on a severed sphere

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

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...

An illustration of various embeddings on the digits dataset.

Gaussian Mixture Models

Examples concerning the sklearn.mixture package.

Density Estimation for a mixture of Gaussians

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

Gaussian Mixture Model Ellipsoids

Plot the confidence ellipsoids of a mixture of two Gaussians with EM and variational Dirichlet ...

Gaussian Mixture Model Sine Curve

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

Gaussian Mixture Model Selection

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

GMM classification

Demonstration of Gaussian mixture models for classification.

Nearest Neighbors

Examples concerning the sklearn.neighbors package.

Nearest Neighbors regression

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

Nearest Neighbors Classification

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

Nearest Centroid Classification

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

Kernel Density Estimation

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

Kernel Density Estimate of Species Distributions

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

Simple 1D Kernel Density Estimation

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.

Label Propagation learning a complex structure

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

Decision boundary of label propagation versus SVM on the Iris dataset

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

Label Propagation digits: Demonstrating performance

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

Label Propagation digits active learning

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

Support Vector Machines

Examples concerning the sklearn.svm package.

Non-linear SVM

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

Support Vector Regression (SVR) using linear and non-linear kernels

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

SVM: Maximum margin separating hyperplane

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

SVM: Separating hyperplane for unbalanced classes

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

SVM with custom kernel

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

SVM-Anova: SVM with univariate feature selection

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

SVM: Weighted samples

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

One-class SVM with non-linear kernel (RBF)

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

Plot different SVM classifiers in the iris dataset

Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only ...

SVM-Kernels

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

SVM Margins Example

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

Scaling the regularization parameter for SVCs

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

RBF SVM parameters

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

Decision Trees

Examples concerning the sklearn.tree package.

Decision Tree Regression

A 1D regression with decision tree.

Multi-output Decision Tree Regression

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

Plot the decision surface of a decision tree on the iris dataset

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