ds-supervised-modeling

What problem does it solve?

Streamline the end-to-end supervised learning workflow, from problem framing and algorithm selection to training, evaluation, interpretation, and deployment readiness.

Core Features & Use Cases

• Algorithm selection framework for regression and classification tasks, with guidance on when to prefer linear models, tree ensembles, or heuristic approaches.
• End-to-end workflow support: train/test splits, model training, cross-validation, evaluation, and interpretation.
• Fairness auditing and bias checks to identify disparities across sensitive groups.
• Model comparison and hyperparameter tuning to help choose the best performing model for a task.

Quick Start

Load a labeled dataset, split into train and test, train a baseline model, and evaluate its performance to establish a starting point.