Examples

Release:0.5 Date:October 19, 2010

General examples

General-purpose and introductory examples for the scikit.

• Pipeline Anova SVM
• Parameter estimation using grid search with a nested cross-validation
• Logistic Regression
• Classification of text documents
• Classification of text documents using sparse features
• Gaussian Naive Bayes
• Plot classification probability
• Confusion matrix
• Ledoit-Wolf vs Covariance simple estimation
• Recognizing hand-written digits
• Univariate Feature Selection
• Blind source separation using FastICA
• FastICA on 2D point clouds
• Linear Discriminant Analysis & Quadratic Discriminant Analysis
• Linear Discriminant Analysis & Quadratic Discriminant Analysis
• Nearest Neighbors
• PCA 2d projection of of Iris dataset
• Precision-Recall
• Receiver operating characteristic (ROC)
• Receiver operating characteristic (ROC) with cross validation
• Train error vs Test error
• Recursive feature elimination
• Recursive feature elimination with cross-validation
• Finding structure in the stock market

Clustering

Examples concerning the scikits.learn.cluster package.

• Demo of affinity propagation clustering algorithm
• Segmenting the picture of Lena in regions
• A demo of the mean-shift clustering algorithm
• Spectral clustering for image segmentation

Generalized Linear Models

Examples concerning the scikits.learn.glm package.

• Lasso regression example
• Lasso on dense and sparse data
• Lasso parameter estimation with path and cross-validation
• Automatic Relevance Determination Regression (ARD)
• Bayesian Ridge Regression
• Least Angle Regression (LAR)
• Lasso and Elastic Net
• Lasso with Least Angle Regression
• Ordinary Least Squares

Gaussian Mixture Models

Examples concerning the scikits.learn.gmm package.

• Gaussian Mixture Model Ellipsoids
• Density Estimation for a mixture of Gaussians

Support Vector Machines

Examples concerning the scikits.learn.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-Anova: SVM with univariate feature selection
• Non-linear SVM
• Support Vector Regression (SVR) using RBF kernel
• SVM: Separating hyperplane with weighted classes