Automate CRISP-DM 4.3 model building with MLflow tracking and documentation.
npx skills add https://github.com/thbraet/claude-template --skill build-model-thbraet
Please help me install this Agent Skill. Skill: build-model Source: https://github.com/thbraet/claude-template/tree/main/skills/build-model Command: npx skills add https://github.com/thbraet/claude-template --skill build-model-thbraet
This CRISP-DM 4.3 workflow provides a repeatable process for constructing baseline and candidate models, with end-to-end experiment tracking and documentation.
Provide your prepared data and run the build-model workflow to generate the notebook and report.
High-intent search queries and answers about installing and using this skill.
Model building in this CRISP-DM 4.3 workflow involves constructing baseline and candidate models with MLflow tracking. It automates experiment logging and generates a reproducible notebook alongside a formal model-building report.
Tracking CRISP-DM model building experiments with MLflow is handled automatically by this workflow. It logs model parameters, outcomes, and artifacts during candidate model exploration and outputs a summary document at docs/crisp-dm/4-modeling/.
Yes, you need prepared data before starting CRISP-DM 4.3 model building. The workflow requires data pipeline outputs from prior phases to construct baseline models, generate the notebook at notebooks/4.3-model-building.ipynb, and track artifacts properly.
Yes, you can generate a model building report and notebook together for data science projects. This workflow produces a reproducible notebook at notebooks/4.3-model-building.ipynb and a formal report at docs/crisp-dm/4-modeling/4.3-model-building.md simultaneously.
Experiment tracking handles artifact management by logging candidate models and data pipeline outputs directly to MLflow. This ensures proper traceability from data preparation through evaluation and maintains reproducibility across the generated artifacts.
Automating baseline model construction in a CRISP-DM workflow is limited to projects requiring baseline and candidate models with MLflow logging. It focuses on the 4.3 modeling phase and does not handle data preparation or final deployment tasks.
thbraetAutomate data cleaning for CRISP-DM 3.2 workflows with notebooks and summaries.
thbraetAssess trained models beyond aggregate metrics for CRISP-DM workflows.
Agentic-AssetsOrchestrate end-to-end ML pipelines from data ingestion to deployment.
thbraetDocument data acquisition and initial inventories for CRISP-DM workflows.
