Predicting AI Specialization Domains from Practitioner Skill Profiles: A Ten-Class Multiclass Classification Study with Gradient Boosting and SHAP Explainability

Published

18 June 2026

Venue

IJIRT

DOI

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Abstract

The rapid expansion of artificial intelligence as a professional discipline has created an urgent need for data-driven career guidance systems capable of matching practitioners to specialization domains based on measurable skill and experience profiles. This study frames AI domain recommendation as a supervised ten-class classification problem and evaluates five state-of-the-art classifiers — Logistic Regression, Random Forest, XGBoost, LightGBM, and CatBoost — on a dataset of 30,000 AI practitioner profiles across 30 features spanning technical skill scores, experience metrics, productivity indicators, and tooling preferences. Rigorous experimental controls include explicit exclusion of target-leaking domain labels, stratified 60/20/20 train-validation-test splitting, median imputation for three low-missingness columns, and 5-fold stratified cross-validation. LightGBM achieves the highest performance with test accuracy of 83.67%, macro-F1 of 0.7050, weighted F1 of 0.8250, and macro ROC-AUC of 0.9352. SHAP-based explainability identifies largest model parameters million, skill consistency score, and mathematics for ai score as the dominant global predictors. Severe class imbalance between Generative AI (21.4%) and Reinforcement Learning (1.4%) drives differential per-class performance, with Finance AI (F1 = 0.129) and Healthcare AI (F1 = 0.198) remaining severely under-predicted despite class-weighted training. This work establishes the first replicable, leakage-free methodology and performance baseline for AI specialization domain recommendation.