Error metrics classification model

classification
evaluation_metric

#1

You have built a classification model with 90% accuracy but your client is

not happy because False Positive rate was very high then what will you do?


#2

Hello @biswajitsahoo.91

Change the metric from Accuracy to AUC ROC (Area Under Curve - Receiver operating characteristic). Train a better model that gives high AUC values (ranges from 0-1)