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skmatter v0.4.dev29+ga1a316a

  • Getting started
  • Installation
  • API Reference
  • Examples
  • Contributing
    • Changelog
    • References
  • GitHub
  • Getting started
  • Installation
  • API Reference
  • Examples
  • Contributing
  • Changelog
  • References
  • GitHub

Section Navigation

  • PCovR and KernelPCovR
    • Choosing Different Regressors for PCovR
    • Construct a PCovR Map
    • The Importance of Data Scaling in PCovR / KernelPCovR
    • The Benefits of Kernel PCovR for the WHO Dataset
  • PCovC and KernelPCovC
    • Comparing PCovC with PCA and LDA
    • KPCovC Hyperparameter Tuning
    • PCovC Hyperparameter Tuning
    • Comparing KPCovC with KPCA
  • Feature and Sample Selection
    • Using scikit-matter selectors with scikit-learn pipelines
    • PCovR-Inspired Feature Selection
    • Generalized Convex Hull construction for the polymorphs of ROY
    • Feature Selection on the WHO Dataset
  • Regression
    • Regression with orthogonal projector/matrices
    • Ridge2FoldCV for data with low effective rank
  • Feature Reconstruction Measures
    • Pointwise Local Reconstruction Error
    • Global Feature Reconstruction Error (GFRE) and Distortion (GFRD)
    • Pointwise GFRE applied on RKHS features
  • Neighbors
    • Sparse KDE examples
    • Probabilistic Analysis of Molecular Motifs (PAMM)
  • Examples
  • Regression

Regression#

Regression with orthogonal projector/matrices

Regression with orthogonal projector/matrices

Ridge2FoldCV for data with low effective rank

Ridge2FoldCV for data with low effective rank

Download all examples in Python source code: regression_python.zip

Download all examples in Jupyter notebooks: regression_jupyter.zip

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