This is the html version of user guide. A PDF version for pinting can be found here.

Supervised learning

• Support Vector Machines
  • Classification
  • Regression
  • Density estimation, outliers detection
  • Support Vector machines for sparse data
  • Complexity
  • Tips on Practical Use
  • Kernel functions
    • Custom Kernels
      • Using python functions as kernels
      • Passing the gram matrix
  • Mathematical formulation
    • SVC
    • NuSVC
  • Frequently Asked Questions
  • Implementation details
• Generalized Linear Models
  • Ordinary Least Squares (OLS)
    • OLS Complexity
  • Ridge Regression
    • Ridge Complexity
  • Lasso
  • Elastic Net
  • LARS algorithm and its variants
  • LARS Lasso
    • Getting the full path
    • Mathematical formulation
  • Bayesian Regression
    • Bayesian Ridge Regression
  • Automatic Relevance Determination - ARD
    • Mathematical formulation
• Nearest Neighbors
  • Classification
  • Regression
  • Efficient implementation: the ball tree
• Feature selection
  • Univariate feature selection
    • Feature scoring functions
      • For classification
      • For regression
    • Examples

Unsupervised learning

• Gaussian mixture models
  • GMM classifier
    • Examples
• Clustering
  • Affinity propagation
  • Mean Shift
  • K-means
  • Spectral clustering

Model Selection

• Grid Search
  • GridSearchCV
    • Examples

Cross Validation

• Cross-Validation
  • Examples
    • Leave-One-Out - LOO
    • Leave-P-Out - LPO
    • K-fold
    • Stratified K-Fold
    • Leave-One-Label-Out - LOLO
    • Leave-P-Label-Out