The evaluation metric for this competition is the
F1-Score
of class 1, i.e.
default_payment.next.month=1
.
In statistical analysis of binary classification, the F1 score (also F-score or F-measure) is a measure of a test's accuracy. It considers both the precision p and the recall r of the test to compute the score: p is the number of correct positive results divided by the number of all positive results returned by the classifier, and r is the number of correct positive results divided by the number of all relevant samples (all samples that should have been identified as positive). The F1 score is the harmonic average of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0.
\[ F1 = 2\frac{p \cdot r}{p+r}\ \ \mathrm{where}\ \ p = \frac{tp}{tp+fp},\ \ r = \frac{tp}{tp+fn} \]
Submission Format
For every client in the dataset
, submission files should contain two columns: client ID and prediction about his/her default payment in next month.
The file should contain a header and have the following format:
ID,default.payment.next.month
20001,1
20002,0
20003,0
20004,0
......