Here is my code:

score = metrics.f1_score(y_test[0:], y_pred, pos_label=list(set(y_test)))

And here are my dimensions/shapes, which I print before executing the score line (the line producing the error), and they get printed:

Original df shape:  (6944, 13)
x_train (Training Features) Shape: (4860, 12)
y_train (Training Labels) Shape: (4860,)
x_test (Testing Features) Shape: (2084, 12)
y_test (Testing Labels) Shape: (2084,)
features length: 38
Accuracy: 0.731765834933
y_pred shape:  (2084,)
[1 1 1 ..., 1 0 0]
y_test shape:  (2084,)
1330    1
2543    1
many other 0,1 values here! Deleted for the post clarity
3025    0
5776    1 

I am getting following error:

ValueError                                Traceback (most recent call last)
<ipython-input-440-9059291258bf> in <module>()
     44 from sklearn import metrics
---> 46 score = metrics.f1_score(y_test[0:], y_pred, pos_label=list(set(y_test)))
     48 #print(y_pred)
ValueError: all the input arrays must have same number of dimensions

So I am giving wrong dimensions to metrics.f1_score function. How could I pass the y_test and y_pred in the right form?


1 Answer 1


I think you misinterpreted the meaning of pos_label. It is used for choosing the positive label; its default value is set to $1$, which means the method calculate precision, recall and therefore f1 score by assuming class 1 as positive class, and class 0 as negative class. You seem to interpret it as possible labels, because you input unique class labels in the test set. Just remove pos_label argument from your call.

  • $\begingroup$ But with the dimensions (and shapes) I have, is this part ( y_test[0:], y_pred, ) right? $\endgroup$
    – ZelelB
    Nov 25, 2018 at 20:37
  • $\begingroup$ yes, it seems right. You also don't need 0: in y_test. Just input y_test. $\endgroup$
    – gunes
    Nov 25, 2018 at 20:38

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