Use this skill whenever the user wants to build, train, evaluate, or iterate on a supervised machine learning or deep learning model. Triggers include: any mention of training or fitting a model with labeled data, dataset preparation, hyperparameter tuning, cross-validation, model checkpointing, experiment tracking, overfitting, loss curves, metrics, or prediction visualization. Also trigger when
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Supervised Machine Learning / Deep Learning Modeling Skill
Principles
What a master ML/DL practitioner keeps in mind regardless of task, scale, or context.
Understand the data before designing the model. What you find in data exploration changes
architecture and training decisions.
Adapt depth to the user's context, not to a fixed template. A quick experiment and a
production pipeline need different levels of rigor. Match the work to what the user actually
needs.
**Ev
[Description truncada. Veja o README completo no GitHub.]
