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
<mailto: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 <mailto: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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