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The authors reanalyze the canonical Palmer Penguins dataset using Computational Taxonomy and Scientific Data Analysis to model Sexual Size Dimorphism (SSD) and mate selection across higher-order (orders 2 to 4) feature interactions. Exhaustive associative screening across morphological feature-sets reveals significant block-structured intra-sex heterogeneity driven primarily by body mass interacting with culmen and flipper dimensions. This latent sub-structure invalidates the standard logistic regression models historically applied to the benchmark, highlighting the failure modes of common generalized linear baselines on heterogeneous tabular data.
Standard logistic regression on the ubiquitous Palmer Penguins benchmark falls apart under higher-order feature analysis, which uncovers severe intra-sex structural heterogeneity that classical classifiers completely miss.
We employ Computational Taxonomy (CT) to reanalyze the penguin data set penguins_lter by validating and addressing two biological issues: Sexual Size Dimorphism (SSD) and mate-selection criteria. Via Scientific Data Analysis (SDA) computing, CT constructs a Taxonomic Hierarchy by splitting Species first and then Sex, without involving Island, to achieve less complexity. This Taxonomic Hierarchy validates SSD as a branch comparison: (Species, Sex = Male)-vs-(Species, Sex = Female), upon which SDA explores all potential pieces of associative information from all covariate feature-sets, including interacting effects from order-2 to order-4, and then confirms them via their idiosyncratic reliability checks. The collective of confirmed information pieces are displayed on a heatmap platform to manifest underlying dynamics of SSD with explicit block-structured heterogeneity found within males and females. SSD dynamics is explained through mechanistic dependence pertaining to one chief factor consisting of up to 8 feature-sets: Body-Mass coupled by combinations of {Culmen-length,Culmen-depth, Flipper-length}, and two minor factors consisting of low-order combinations of {Culmen-length,Culmen-depth, Flipper-length}. Such Intra-Sex heterogeneity invalidates all Logistic regression modeling on SSD in the original paper. Further, we explore potential mate-selection criteria through the data-frame of Nest-ID within-species homogeneity.