select-modeling-techniques

To select modeling techniques for a CRISP-DM project, evaluate candidate methods against data mining goals, data characteristics, and project constraints. This produces a structured technique selection report with a comparison matrix and rationale.

The best way to document baseline modeling technique selection is to generate a Jupyter notebook and a markdown summary in the CRISP-DM 4-modeling directory. This captures the technique comparison matrix, assumptions, and selection rationale.

Technique selection works by assessing candidate methods against defined data mining goals and data characteristics. It generates a baseline artifact stored in notebooks and docs that includes a comparison matrix to guide subsequent modeling steps.

You can use this technique selection workflow for any CRISP-DM 4.1 data mining project. It systematically evaluates candidate methods against your specific project constraints, data characteristics, and data mining goals to produce a tailored selection report.

When choosing modeling techniques, you need to generate a Jupyter notebook with analysis and a markdown document for CRISP-DM phase 4 artifacts. These baseline artifacts must include a comparison matrix, rationale, and modeling assumptions.

You need to document modeling assumptions during baseline technique selection to clearly guide the next steps of your CRISP-DM project. These assumptions, alongside the comparison matrix and rationale, ensure the chosen data mining methods are transparent and validated.