pythontaskviews

Python task views aim to provide guidance on which python packages are relevant for tasks related to a certain data science topic

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Python Task Views: Regression Methods

Scope, Organization, Caveats

The scope aims to cover typical regression methods, not edge cases. Regressions in relation to timeseries data are to be covered in the Econometrics Task View. The entries are grouped together roughly by similarity of purpose (use case) and problem context but there can be obviously substantial overlap

When there is no package identified that appears reasonably stable / mature the corresponding slot is simply empty

Listing here does not imply any sort of assurance about the quality of algorithms, the degree of testing etc. Always test packages and functionalities to a degree proportional to the significance, requirements and sensitivity of your data science task.

Packages offering regression methods functionality

  • The first column indicates the specific method and / or functionality along with a documentation link where the mathematics of the method is documented in detail
  • The second column has links to functionality offered by the lower level packages scipy/numpy/pandas (where available)
  • The third column has links to the standard statistical packages (scikit-learn and statsmodels)
  • The fourth column has links to deep learning packages (where regressions methods are already released)
  • The fifth column is reserved for more specialized projects
Functionality / Documentation Scipy/Numpy/Pandas Scikit-learn/Statsmodels Pytorch/TensorFlow Other
Linear Regression scipy.stats.linregress, numpy.linalg.lstsq Placeholder Placeholder Scipy/Numpy offer low level least squares routines
Generalized Liner Regression Placeholder Placeholder Placeholder Placeholder
Fractional Response Regression Placeholder Placeholder Placeholder Placeholder
Beta / Inflated Beta Regression Placeholder Placeholder Placeholder Placeholder
Polynomial Regression Placeholder Placeholder Placeholder Placeholder
Logistic Regression Placeholder Statsmodels.logit, scikit-learn Placeholder Placeholder
Probit Regression Placeholder Placeholder Placeholder Placeholder
Multinomial Regression Placeholder Placeholder Placeholder Placeholder
Ordinal Regression Placeholder Placeholder Placeholder Placeholder
Stepwise Regression Placeholder Placeholder Placeholder Placeholder
Ridge Regression (Tikhonov Regularization) Placeholder sklearn.linear_model.Ridge Placeholder Linear least squares (Various Solvers) with L2 Regularization
Lasso Regression Placeholder sklearn.linear_model.Lasso Placeholder Coordinate descent with L1 Regularization
Elastic Net Regression Placeholder sklearn.linear_model.ElasticNet Placeholder Coordinate descent with L1 + L2 Regularization
Bayesian linear regression Placeholder   Placeholder Placeholder
Segmented regression Placeholder Placeholder Placeholder Placeholder
Robust regression Placeholder statsmodels.robust Placeholder Placeholder

Notes

  • Tensorflow modules reference 2.0 only