Aggressive deletion of outliers before fraud modeling may be harmfu…

Question

Aggressive deletion of outliers before fraud modeling may be harmful because:

Accepted Answer

Fraud itself can appear as rare extreme behavior — domain-aware treatment beats blind deletion — In fraud, the 'outlier' might be the label you care about — don't blind-delete.

Answer

Outliers never matter

Answer

Trees cannot train with extremes ever

Answer

MLflow forbids outliers

Aggressive deletion of outliers before fraud modeling may be harmful because:

Short Explanation and Infographic

Full Explanation