MELITE: Multi-model Evaluation and Learning for Inference-ready Tabular Experiments

v0.2.3 (2026-06-01)

MELITE is a pre-stable Python toolkit for tabular classification benchmarking, model selection, repeated stratified cross-validation, final model export, and artifact-based inference. It operates at the tabular modeling level by consuming numeric feature matrices and labels, benchmarks SVC, Random Forest and XGBoost classifiers, evaluates model configurations with repeated stratified cross-validation, exports tabular performance summaries, and supports final model retraining and serialization as reusable model artifacts.