Examples

General examples

General-purpose and introductory examples for the scikit.

Plot classification probability

Confusion matrix

Recognizing hand-written digits

The Digit Dataset

Pipelining

Univariate Feature Selection

Classifiers Comparison

The Iris Dataset

Explicit feature map approximation for RBF kernels

Linear and Quadratic Discriminant Analysis with confidence ellipsoid

Multilabel classification

Test with permutations the significance of a classification score

PLS Partial Least Squares

Precision-Recall

Plot randomly generated classification dataset

Recursive feature elimination

Recursive feature elimination with cross-validation

Receiver operating characteristic (ROC)

Receiver operating characteristic (ROC) with cross validation

Train error vs Test error

Classification of text documents using sparse features

Clustering text documents using k-means

Pipeline Anova SVM

Parameter estimation using grid search with a nested cross-validation

Sample pipeline for text feature extraction and evaluation

Classification of text documents: using a MLComp dataset

Examples based on real world datasets

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

Faces recognition example using eigenfaces and SVMs

Outlier detection on a real data set

Species distribution modeling

Visualizing the stock market structure

Compressive sensing: tomography reconstruction with L1 prior (Lasso)

Libsvm GUI

Topics extraction with Non-Negative Matrix Factorization

Wikipedia princial eigenvector

Clustering

Examples concerning the sklearn.cluster package.

Adjustment for chance in clustering performance evaluation

Demo of affinity propagation clustering algorithm

Comparing different clustering algorithms on toy datasets

K-means Clustering

Color Quantization using K-Means

Demo of DBSCAN clustering algorithm

Feature agglomeration

Feature agglomeration vs. univariate selection

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

Empirical evaluation of the impact of k-means initialization

Vector Quantization Example

Segmenting the picture of Lena in regions

A demo of structured Ward hierarchical clustering on Lena image

A demo of the mean-shift clustering algorithm

A demo of the K Means clustering algorithm

Spectral clustering for image segmentation

Hierarchical clustering: structured vs unstructured ward

Covariance estimation

Examples concerning the sklearn.covariance package.

Ledoit-Wolf vs Covariance simple estimation

Ledoit-Wolf vs OAS estimation

Robust covariance estimation and Mahalanobis distances relevance

Outlier detection with several methods.

Robust vs Empirical covariance estimate

Sparse inverse covariance estimation

Decomposition

Examples concerning the sklearn.decomposition package.

Faces dataset decompositions

Blind source separation using FastICA

FastICA on 2D point clouds

Image denoising using dictionary learning

Kernel PCA

Principal Component Analysis

PCA example with Iris Data-set

Comparison of LDA and PCA 2D projection of Iris dataset

Sparse coding with a precomputed dictionary

Ensemble methods

Examples concerning the sklearn.ensemble package.

Feature importances with forests of trees

Pixel importances with a parallel forest of trees

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

Exercises for the tutorials

Cross-validation on diabetes Dataset Exercise

Cross-validation on Digits Dataset Exercise

Digits Classification Exercise

SVM Exercise

Gaussian Process for Machine Learning

Examples concerning the sklearn.gaussian_process package.

Gaussian Processes classification example: exploiting the probabilistic output

Gaussian Processes regression: basic introductory example

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

Generalized Linear Models

Examples concerning the sklearn.linear_model package.

Automatic Relevance Determination Regression (ARD)

Bayesian Ridge Regression

Logistic Regression 3-class Classifier

Lasso and Elastic Net for Sparse Signals

Lasso and Elastic Net

Lasso path using LARS

Lasso model selection: Cross-Validation / AIC / BIC

Logit function

L1 Penalty and Sparsity in Logistic Regression

Path with L1- Logistic Regression

Linear Regression Example

Sparsity Example: Fitting only features 1 and 2

Ordinary Least Squares and Ridge Regression Variance

Orthogonal Matching Pursuit

Polynomial interpolation

Plot Ridge coefficients as a function of the regularization

Plot multi-class SGD on the iris dataset

SGD: Convex Loss Functions

Ordinary Least Squares with SGD

SGD: Penalties

SGD: Maximum margin separating hyperplane

SGD: Separating hyperplane with weighted classes

SGD: Weighted samples

Sparse recovery: feature selection for sparse linear models

Lasso on dense and sparse data

Manifold learning

Examples concerning the sklearn.manifold package.

Comparison of Manifold Learning methods

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

Swiss Roll reduction with LLE

Gaussian Mixture Models

Examples concerning the sklearn.mixture package.

Gaussian Mixture Model Ellipsoids

GMM classification

Density Estimation for a mixture of Gaussians

Gaussian Mixture Model Selection

Gaussian Mixture Model Sine Curve

Nearest Neighbors

Examples concerning the sklearn.neighbors package.

Nearest Neighbors Classification

Nearest Neighbors regression

Semi Supervised Classification

Examples concerning the sklearn.semi_supervised package.

Label Propagation digits: Demonstrating performance

Label Propagation digits active learning

Label Propagation learning a complex structure

Decision boundary of label propagation versus SVM on the Iris dataset

Support Vector Machines

Examples concerning the sklearn.svm package.

SVM with custom kernel

Plot different SVM classifiers in the iris dataset

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

RBF SVM parameters

SVM: Maximum margin separating hyperplane

SVM: Separating hyperplane for unbalanced classes

SVM-Anova: SVM with univariate feature selection

SVM-SVC (Support Vector Classification)

SVM-Kernels

SVM Margins Example

Non-linear SVM

Seleting hyper-parameter C and gamma of a RBF-Kernel SVM

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

SVM: Weighted samples

Decision Trees

Examples concerning the sklearn.tree package.

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

Decision Tree Regression