Wrong False Negative Definition in Confusion Matrix

[Shuobin-P]

Tutorial Wrong definition: If your model predicts a pumpkin as white and it belongs to category 'not-white' in reality we call it a false negative,

Correction: If your model predicts a pumpkin as white and it belongs to category 'not-white' in reality we call it a False Positive,

"FP is the number of negative examples incorrectly classified as positive (False Positives). " Above explanation is based on the '2. Performance Measures' section in the paper "SMOTE: Synthetic Minority Over-sampling Technique" by N. V. Chawla et al., published in the Journal of Artificial Intelligence Research.