This documentation is for scikit-learn version 0.13.1 — Other versions
If you use the software, please consider citing scikit-learn.
General-purpose and introductory examples for the scikit.
Plot classification probability
Classifiers Comparison
Confusion matrix
Recognizing hand-written digits
Pipelining: chaining a PCA and a logistic regression
Univariate Feature Selection
Demonstration of sampling from HMM
Gaussian HMM of stock data
Isotonic Regression
The Johnson-Lindenstrauss bound for embedding with random projections
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
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
Concatenating multiple feature extraction methods
Parameter estimation using grid search with a nested cross-validation
Sample pipeline for text feature extraction and evaluation
Comparison of hashing-based and dictionary based text vectorization
Classification of text documents: using a MLComp dataset
Applications to real world problems with some medium sized datasets or interactive user interface.
Outlier detection on a real data set
Species distribution modeling
Visualizing the stock market structure
Compressive sensing: tomography reconstruction with L1 prior (Lasso)
Faces recognition example using eigenfaces and SVMs
Libsvm GUI
Topics extraction with Non-Negative Matrix Factorization
Wikipedia principal eigenvector
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
Online learning of a dictionary of parts of faces
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
Examples concerning the sklearn.covariance package.
Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood
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
Examples concerning the sklearn.datasets package.
The Digit Dataset
The Iris Dataset
Plot randomly generated classification dataset
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
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
Face completion with multi-output forests
Prediction Intervals for Gradient Boosting Regression
Gradient Boosting regression
Gradient Boosting regularization
Partial Dependence Plots
Hashing feature transformation using Totally Random Trees
Exercises for the tutorials
Cross-validation on diabetes Dataset Exercise
Cross-validation on Digits Dataset Exercise
Digits Classification Exercise
SVM Exercise
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
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
Joint feature selection with multi-task Lasso
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
Comparing various online solvers
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
Examples concerning the sklearn.manifold package.
Comparison of Manifold Learning methods
Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...
Manifold Learning methods on a severed sphere
Multi-dimensional scaling
Swiss Roll reduction with LLE
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
Examples concerning the sklearn.neighbors package.
Nearest Neighbors Classification
Nearest Centroid Classification
Nearest Neighbors regression
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
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
Support Vector Regression (SVR) using linear and non-linear kernels
Support Vector Classification (SVC): scaling the regularization parameter
SVM: Weighted samples
Examples concerning the sklearn.tree package.
Plot the decision surface of a decision tree on the iris dataset
Decision Tree Regression
Multi-output Decision Tree Regression