Comments (1)
dfsnow
commented on June 22, 2024
We tested out using embeddings of categoricals via the same process as our linear baseline model. However, the embeddings have worse performance than just letting LightGBM handle the categoricals natively. Additionally, the dependencies for the embed package are quite heavy, so as of #190 we've removed embeddings entirely (including from the linear model).
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Related Issues (20)
- Add workflow/process for tagging models with `run_type` HOT 1
- Add SHAP maps for location and proximity features
- Diagnostics: "Challenge groups" HOT 1
- Create aggregate maps using comps output HOT 2
- Refactor comps to calculate tree weights on a per-card basis HOT 1
- Add sales val run_id to res model outputs
- Add sale ratio column to desk review sheets HOT 1
- Flesh out characteristics in desk review workbooks
- Refactor `export` stage to use a config dict representing the workbook structure
- Comps check HOT 1
- Update `ingest` stage to use `noctua` `unload = TRUE` option HOT 3
- Improve modeling multi-cards
- Spike upgrading comps algorithm with taichi HOT 1
- Spike model rebuild in Python/polars
- Create explainer/diagrams for multi-PIN and multi-card aggregations
- Investigate merging the residential and condo model codebases
- Adjust `model_fetch_run()` to optionally fetch input data
- Update comps algorithm to save `instruno` in addition to `parid` HOT 1
- Generate historical model API workbooks
- Edit `dvc.yaml` to include files invoked as deps
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