Please consider citing the scikit-learn.
General-purpose and introductory examples for the scikit.
Plot classification probability
Confusion matrix
Recognizing hand-written digits
Univariate Feature Selection
Linear and Quadratic Discriminant Analysis with confidence ellipsoid
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 MiniBatchKmeans
Pipeline Anova SVM
Gaussian Naive Bayes
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
Applications to real world problems with some medium sized datasets or interactive user interface.
Species distribution modeling
Faces recognition example using eigenfaces and SVMs
Finding structure in the stock market
Libsvm GUI
Topics extraction with Non-Negative Matrix Factorization
Wikipedia princial eigenvector
Examples concerning the sklearn.cluster package.
Adjustment for chance in clustering performance evaluation
Demo of affinity propagation clustering algorithm
Color Quantization using K-Means
Demo of DBSCAN clustering algorithm
Feature agglomeration vs. univariate selection
A demo of K-Means clustering on the handwritten digits data
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.
Ledoit-Wolf vs Covariance simple estimation
Ledoit-Wolf vs OAS estimation
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
Comparison of LDA and PCA 2D projection of Iris dataset
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
Lasso and Elastic Net
Lasso path using LARS
Lasso model selection: Cross-Validation / AIC / BIC
Path with L1- Logistic Regression
Ordinary Least Squares
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
Lasso regression example
Lasso on dense and sparse data
Logistic Regression
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
Examples concerning the sklearn.mixture package.
Gaussian Mixture Model Ellipsoids
GMM classification
Density Estimation for a mixture of Gaussians
Gaussian Mixture Model Sine Curve
Examples concerning the sklearn.neighbors package.
Nearest Neighbors Classification
Nearest Neighbors regression
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)
SVM: Maximum margin separating hyperplane
SVM: Separating hyperplane for unbalanced classes
SVM-Anova: SVM with univariate feature selection
Non-linear SVM
Support Vector Regression (SVR) using linear and non-linear kernels
SVM: Weighted samples