pretty simple question here but just can't seem to find the answer in the normally great documentation for sklearn.
I am working with binary classifiers, but we can just assume i am using
LogisticRegression, and I was wondering if there is a general way to have the classifier select, say only 10 best (most sure) data points?
For example, say I train a set with 500K data points, and my test set has 10K lines, and out of the 10K, I just want to choose 10 that have the highest chance of being true positives. Does this make sense?
I have read about, and have been playing with the
class_weights attribute, which works well for giving more/less weight to each of the binary outcome classes, but its not quite working for what I want in that it always give different number of position predictions, and I can't really tell how sure the classifier is about each one of those.