and here is an example output ( dont wonder, the test data is just an
subsample of the training set, that why there are everywhere 1.0 ).
AUC = 0.0
precision recall f1-score support
0.0 1.00 1.00 1.00 2
1.0 1.00 1.00 1.00 1
avg / total 1.00 1.00 1.00 3
auc is [0.039985222019318888, 0.98263621819591684, 0.99450177921770244]
expected is [1.0, 0.0, 0.0]
On 5 August 2015 at 10:54, Herbert Schulz <hrbrt....@gmail.com> wrote:
> Maybe i didn't explained it very well sorry.
>
> I just have 1 column as a target. The last "post" i did, was just a
> converting from all 0's to 1's and all 1's to 0's. But the auc and the
> expected are from the same date which is converted. So actually it should
> be
>
> auc is [0.9777752710670069, 0.01890450385597026, 0.0059624156214325846,
> 0.05391726570661811]
> expected is [0.0, 1.0, 1.0, 1.0]
>
> here for the auc and the 2-4 values something like 0.97.... and on the
> first value 0.01...
>
>
>
> predicted=clf.predict_proba(X_test)
> predi=[]
>
> classi=[]
>
>
> for i in range(len(predicted)):
> auc.append(predicted[i][0])
>
> print "auc is",auc
> print "expected is", y_test
> roc= metrics.roc_auc_score(y_test, auc)
>
> print roc
>
> So there should be a failure in my data preprocessing or?
>
> or can i just turn the expected vector? I think that would be a good idea
> if I'm using the normal data.
>
> best
>
>
>
>
>
>
> On 4 August 2015 at 17:38, Andreas Mueller <t3k...@gmail.com> wrote:
>
>> You should select the other column from predict_proba for auc.
>>
>>
>>
>> On 08/04/2015 10:54 AM, Herbert Schulz wrote:
>>
>> Thanks for the answer!
>>
>> hmm its possible, I just make a little example:
>>
>> auc is [0.9777752710670069, 0.01890450385597026, 0.0059624156214325846,
>> 0.05391726570661811]
>> expected is [0.0, 1.0, 1.0, 1.0]
>> but this is already with changed values, in the test set i set every
>> value 0->1 and 1 to 0.
>>
>> SO there is the misstake? it seems that i should "turn" the expected
>> vector y_test ?
>>
>> On 4 August 2015 at 16:36, Artem <barmaley....@gmail.com> wrote:
>>
>>> Hi Herbert
>>>
>>> The worst value for AUC is 0.5 actually. Having values close to 0 means
>>> than you can get a value as close to 1 by just changing your predictions
>>> (predict class 1 when you think it's 0 and vice versa). Are you sure you
>>> didn't confuse classes somewhere along the lines? (You might have chosen
>>> the wrong column from predict_proba's result, for example)
>>>
>>> On Tue, Aug 4, 2015 at 4:51 PM, Herbert Schulz <hrbrt....@gmail.com>
>>> wrote:
>>>
>>>> Hey,
>>>>
>>>> I'm computing the AUC for some data...
>>>>
>>>>
>>>> The classification target is 1 or 0. And i have a lot of 0's ( 5600)
>>>> and just 700 1's as a target.
>>>>
>>>> My AUC is about 0.097...
>>>>
>>>> where y_test are a vector containing 1's and 0's and auc is containg
>>>> the predict_proba values
>>>>
>>>> roc= metrics.roc_auc_score(y_test, auc).
>>>>
>>>>
>>>> Actually this value seems way to bad, because my ballance accuracy is
>>>> about 0.77... i thought that I'm Doing maybe something wrong.
>>>>
>>>>
>>>> report:
>>>>
>>>> precision recall f1-score support
>>>>
>>>> 0.0 0.95 0.91 0.93 537
>>>> 1.0 0.49 0.63 0.55 73
>>>>
>>>> avg / total 0.89 0.88 0.88 610
>>>>
>>>>
>>>>
>>>>
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