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README.md

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@@ -5,11 +5,11 @@ A Python implementation of Jerome Friedman's Multivariate Adaptive Regression Sp
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in the style of scikit-learn. The py-earth package implements Multivariate Adaptive Regression Splines using Cython and provides an interface that is compatible with scikit-learn's Estimator, Predictor, Transformer, and Model interfaces. For more information about
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Multivariate Adaptive Regression Splines, see the references below.
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##Now With Missing Data Support!
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## Now With Missing Data Support!
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The py-earth package now supports missingness in its predictors. Just set `allow_missing=True` when constructing an `Earth` object.
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##Requesting Feedback
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## Requesting Feedback
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If there are other features or improvements you'd like to see in py-earth, please send me an email or open or comment on an issue. In particular, please let me know if any of the following are important to you:
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7. Better support for large data sets
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8. Iterative reweighting during fitting
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##Installation
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## Installation
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Make sure you have numpy and scikit-learn installed. Then do the following:
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sudo python setup.py install
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```
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##Usage
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## Usage
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```python
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import numpy
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from pyearth import Earth
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pyplot.show()
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```
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##Other Implementations
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## Other Implementations
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I am aware of the following implementations of Multivariate Adaptive Regression Splines:
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The R package earth was most useful to me in understanding the algorithm, particularly because of Stephen Milborrow's
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thorough and easy to read vignette (http://www.milbo.org/doc/earth-notes.pdf).
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##References
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## References
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1. Friedman, J. (1991). Multivariate adaptive regression splines. The annals of statistics,
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19(1), 1–67. http://www.jstor.org/stable/10.2307/2241837

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