Optimal Two Step Prediction: Adaptive Validation
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Demo_AVp.py
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README.md
general_functions.py
general_tools.py
lasso_scenarii.py
leukemia_demo.py
noise_estimation_functions.py

README.md

AVp

##Optimal Two Step Prediction: Adaptive Validation

This code is associated with the article:

"Optimal two-step prediction in regression" by Didier Chételat, Johannes Lederer, Joseph Salmon

The correpsonding paper is available on ArXiV at http://arxiv.org/abs/1410.5014

The Demo_AVP.py provides an exemple of the Adaptive Validation on two cases:

  • the Lasso

  • the Thresholded Ridge Regression

##Dependencies AVp is tested to work under Python 2.7.

The required dependencies to build the software are NumPy >= 1.6.2, SciPy >= 0.9 and a working C/C++ compiler, and of course scikit-learn > 0.15

For running the examples Seaborn>= 0.4.0 is required.