ml-modeling

What problem does it solve?

This Skill provides a comprehensive approach to machine learning, from simple baseline models to production-ready models with robust validation and explainability.

Core Features & Use Cases

• Baseline First: Evaluates simple models before moving to more complex ones.
• Validation: Utilizes Stratified K-Fold Cross Validation for robust model validation.
• Desbalanceo Handling: Adapts to imbalanced datasets using F1-Score, Precision-Recall, AUC-ROC.
• Feature Leakage: Ensures no feature leaks information from the future.
• Model Offering: Provides interpretable models, ensembles, and auto-tuning for optimal performance.
• Use Case: When you need to train models, select algorithms, evaluate metrics, or make predictions.

Quick Start

Use the ml-modeling skill to train a machine learning model on your dataset 'data.csv'.