Over-relying on biased feature importance metrics is a critical pitfall in machine learning that can lead to flawed interpretations and poor business decisions. While these metrics offer a seemingly simple way to understand complex models, their inherent biases can misrepresent the true influence of data features, creating a distorted view of what drives model predictions.
Improper temporal feature extraction—specifically, creating features that inadvertently leak information from the future into model training—can severely compromise the validity of time series machine learning models. This phenomenon, often known as temporal leakage or future leak, leads to over-optimistic performance and ultimately, models that fail when applied to real-world, unseen data. Why Is Temporal Feature Extraction Prone to Leakage? Time
Correlation blindness in multivariate analysis refers to the failure to detect or properly address interdependencies and hidden relationships among variables, which can lead to false conclusions, missed insights, and misleading recommendations in data-driven environments. What is Correlation Blindness? In multivariate analysis, analysts often examine multiple variables at once to discover relationships that could not be detected