Ok, so let me rephrase the problem a little bit. Given a machine learning
model built upon scikit-learn, how can I classify new instances but then
choose only those with the highest confidence? How do we define
"confidence" and how to generate it (if it is not generated automatically
by scikit-learn)? What should I change in this approach when I have more
that 2 classes?
On Tue, Aug 19, 2014 at 9:30 PM, Adamantios Corais <
adamantios.cor...@gmail.com> wrote:
> I see. Would it be a good idea to select only those with very high or very
> low probability (class 0 and 1 respectively), or this doesn't make sense at
> all? In addition, may I ask to provide some hints on how to implement that?
> Thanks in advance!
>
>
> On Tue, Aug 19, 2014 at 9:10 PM, Lars Buitinck <larsm...@gmail.com> wrote:
>
>> 2014-08-19 20:07 GMT+02:00 Adamantios Corais <adamantios.cor...@gmail.com
>> >:
>> > Great. And what about the confidence error? I mean, how should I select
>> a
>> > subset of classified data points such that the probability they belong
>> to
>> > any class is high whereas the confidence error is 95% or above?
>>
>> Sorry, I hadn't seen that in your original message. You mean you want
>> confidence intervals on the probability estimates? Those are not
>> computed/returned. There has been talk of implementing those some time
>> ago, but I don't think anything came of it.
>>
>> You could look at statsmodels (but I've only casually used that
>> myself, so no guarantees).
>>
>>
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