lightning
  • Introduction
  • References
  • Examples
  • Site
      • Examples
        • SGD: Convex Loss Functions
        • Signal recovery by 1D total variation
        • Robust regression
        • Trace norm
        • SAGA: Weighted samples
        • Classification of text documents
        • Sensitivity to hyper-parameters in SVRG
        • Sparse non-linear classification
        • L2 solver comparison
      • Classification
        • lightning.classification.AdaGradClassifier
        • lightning.classification.CDClassifier
        • lightning.classification.FistaClassifier
        • lightning.classification.KernelSVC
        • lightning.classification.LinearSVC
        • lightning.classification.SDCAClassifier
        • lightning.classification.SAGClassifier
        • lightning.classification.SAGAClassifier
        • lightning.classification.SGDClassifier
        • lightning.classification.SVRGClassifier
      • Regression
        • lightning.regression.AdaGradRegressor
        • lightning.regression.CDRegressor
        • lightning.regression.FistaRegressor
        • lightning.regression.LinearSVR
        • lightning.regression.SAGRegressor
        • lightning.regression.SAGARegressor
        • lightning.regression.SDCARegressor
        • lightning.regression.SGDRegressor
        • lightning.regression.SVRGRegressor
      • Ranking
        • lightning.ranking.PRank
        • lightning.ranking.KernelPRank
      • Introduction
        • Primal coordinate descent
        • Dual coordinate ascent
        • FISTA
        • Stochastic gradient method (SGD)
        • AdaGrad
        • Stochastic averaged gradient (SAG and SAGA)
        • Stochastic variance-reduced gradient (SVRG)
        • PRank
          • Examples
            • SGD: Convex Loss Functions
            • Signal recovery by 1D total variation
            • Robust regression
            • Trace norm
            • SAGA: Weighted samples
            • Classification of text documents
            • Sensitivity to hyper-parameters in SVRG
            • Sparse non-linear classification
            • L2 solver comparison
          • Classification
            • lightning.classification.AdaGradClassifier
            • lightning.classification.CDClassifier
            • lightning.classification.FistaClassifier
            • lightning.classification.KernelSVC
            • lightning.classification.LinearSVC
            • lightning.classification.SDCAClassifier
            • lightning.classification.SAGClassifier
            • lightning.classification.SAGAClassifier
            • lightning.classification.SGDClassifier
            • lightning.classification.SVRGClassifier
          • Regression
            • lightning.regression.AdaGradRegressor
            • lightning.regression.CDRegressor
            • lightning.regression.FistaRegressor
            • lightning.regression.LinearSVR
            • lightning.regression.SAGRegressor
            • lightning.regression.SAGARegressor
            • lightning.regression.SDCARegressor
            • lightning.regression.SGDRegressor
            • lightning.regression.SVRGRegressor
          • Ranking
            • lightning.ranking.PRank
            • lightning.ranking.KernelPRank

Examples¶

These are some examples using the lightning machine learning library.

SGD: Convex Loss Functions

SGD: Convex Loss Functions¶

Signal recovery by 1D total variation

Signal recovery by 1D total variation¶

Robust regression

Robust regression¶

Trace norm

Trace norm¶

SAGA: Weighted samples

SAGA: Weighted samples¶

Classification of text documents

Classification of text documents¶

Sensitivity to hyper-parameters in SVRG

Sensitivity to hyper-parameters in SVRG¶

Sparse non-linear classification

Sparse non-linear classification¶

L2 solver comparison

L2 solver comparison¶

Download all examples in Python source code: auto_examples_python.zip

Download all examples in Jupyter notebooks: auto_examples_jupyter.zip

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