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Scikit-learn estimators based on projection pursuit.

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# Projection Pursuit [![Travis Status](]( [![Coverage Status](](

This repository is home to a couple [scikit-learn]( estimators based on Jerome Friedman’s generalizations[1] of his and Werner Stuetzle’s Projection Pursuit Regression algorithm[2][3]. A regressor capable of multivariate estimation and a classifier based on regression to a one-hot multivariate representation are included.

## Installation and Usage The package by itself comes with a single module containing the estimators. Before installing the module you will need numpy, scipy, scikit-learn, and matplotlib. To install the module execute:

`shell pip install projection-pursuit ` or `shell $ python install `

If the installation is successful, you should be able to execute the following in Python: `python >>> from skpp import ProjectionPursuitRegressor >>> estimator = ProjectionPursuitRegressor() >>>, 1), np.arange(10)) `

Sphinx is run via continuous integration to generate [the API](

For a few usage examples, see the examples and benchmarks directories. For an intuition of what the learner is doing, try running For comparisons to other learners and an intuition of why you might want to try PPR, try the benchmarks. For a deep dive in to the math and an explanation of exactly how and why this works, see [math.pdf](

## References

  1. Friedman, Jerome. (1985). “Classification and Multiple Regression Through Projection Pursuit.”
  2. Hastie, Tibshirani, & Friedman. (2016). The Elements of Statistical Learning 2nd Ed., section 11.2.
    1. Projection pursuit regression

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