#Description
2nd place submission to the MEG decoding competition
https://www.kaggle.com/c/decoding-the-human-brain
Heikki.Huttunen@tut.fi, Jul 29th, 2014
The model is a hierarchical combination of logistic regression and random forest. The first layer consists of a collection of 337 logistic regression classifiers, each using data either from a single sensor (31 features) or data from a single time point (306 features). The resulting probability estimates are fed to a 1000-tree random forest, which makes the final decision.
The model is wrapped into the LrCollection class. The prediction is boosted in a semisupervised manner by iterated training with the test samples and their predicted classes only. This iteration is wrapped in the class IterativeTrainer.
Requires sklearn, scipy and numpy packages.
Platform: Tested on Scientific Linux release 6.5 and Ubuntu 14.04 LTS.
Example usage:
python train.py
python predict.py
#License
Copyright (c) 2014, Heikki Huttunen
Department of Signal Processing
Tampere University of Technology
Heikki.Huttunen@tut.fi
All rights reserved.
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