I am running a searchlight analysis and I encountered a strange feature of the 
resulting classification accuracies. All of the non-zero accuracies have only 
one decimal point of precision (e.g., .5, .6., .7., .8, etc.). Unfortunately, 
I'm not able to figure out what aspect of my code is leading to this truncating 
or rounding of the accuracies.

I attached my code below, but here's some information about my analysis. Please 
let me know if you would like me to provide any other information about the 
analysis. My searchlight repeats a 10-fold cross-validation procedure for a 
linear support vector classifier with default parameters. The number of classes 
is 2 and the total number of samples is roughly 240. The sequence of samples is 
randomized and balanced so that there is an equal number of instances of the 
two classes in each of the 10 folds. The searchlight also applies the 
mean_sample() postproc so that the resulting classification accuracies are 
averaged over the cross-validation folds.

As mentioned above, I'm unsure what about my script is leading to the 
rounding/truncating of the accuracies. Most of the script is copied from one of 
the PyMVPA searchlight tutorials, which further adds to my confusion, since the 
tutorial searchlight clearly outputs accuracies with greater than 1-decimal 
I would greatly appreciate any ideas you might have about what could be causing 
this problem or how to address it.

Thank you for your time.


Tyler Adkins
PhD Pre-candidate | Cognition and Cognitive Neuroscience
University of Michigan Department of Psychology
530 Church Street Ann Arbor, MI 48109-1043
Email: adkin...@umich.edu
Office: 3036 East Hall
Lab: B018 East Hall

Attachment: pymvpa_searchlight.py
Description: Binary data

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