In high-stakes domains like BFSI (Banking, Financial Services, and Insurance) and Healthcare, the most valuable predictive signals are often hiding in plain sight. Many data teams follow a standard pruning pipeline: calculate the univariate AUC for every feature, and if a feature scores near 0.5 (random noise), it gets tossed. This decision is quietly killing your model's performance. The Linear Trap vs. The Tree Power The problem is a confusion between marginal and conditional effects...