Sure will do thank you. I had a question, unrelated to the cross-validation
issue. I have a training set with skewed distribution of positive and negative
examples in which negative examples far out-number positive ones. The problem
setting is 2 class text classification.
1. How can I specify the Learning model to penalize false negatives more than
false positives? Could we use the same method with different classifiers to
achieve this effect?
2. My second question relates to interface consistency across Scikit-Learn code
base. Has the interface been designed in such a way that once we load the data
into a format that works with one learning model ( maybe a naive-bayes
classifier) , the same data can be used for other learning models,
dimensionality reduction, clustering etc...?
Thanks,
Nikhil
Sent from my iPhone
On Apr 28, 2015, at 11:04 PM, Sebastian Raschka
<se.rasc...@gmail.com<mailto:se.rasc...@gmail.com>> wrote:
Hi, Nikhil,
you could use stratified k-fold cross validation, which preserves the
"original" class proportions. An example can be found here:
http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.StratifiedKFold.html
Best,
Sebastian
On Apr 28, 2015, at 10:40 PM,
nmura...@masonlive.gmu.edu<mailto:nmura...@masonlive.gmu.edu> wrote:
Hello,
I am very new to scikit-learn and am trying to run cross-validation on a data
frame consisting of text features, classification class. I am trying to perform
text data classification. It is a 2-class classification problem where the
distribution between positive and negative instances is extremely skewed ( we
want to keep it that way on purpose ). Is there a specific cross-validation
type in scikit-learn, where I am able to split each of the K-folds so that each
fold has the same proportion of the positive and negative examples? Meaning if
I have :
100 Positive instances
1000 Negative instances,
would it be possible for me to run a 10 fold Cross-validation where each fold
has 10 +ve and 100 -ve examples randomly chosen from the set, held out as the
validation set?
Some sample code or a link with the same would be helpful.
Thanks,
Nikhil
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