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This year, we're going to consolidate all diagnostic reporting into a single Quarto document that gets created for each model run. The document will knit at the end of each run, once the model is finished training, has created performance statistics, etc. The output PDF (or HTML) will be uploaded to S3 along with the other model artifacts, then linked in SNS completion notifications.
This Quarto doc will be the primary way we evaluate individual model performance. Cross-model comparison will still be done via Tableau.
model_qc.qmd might be a good starting place for defining the Quarto doc itself.
Tasks
Update the 05-finalize.R step of the model so that it generates a Quarto doc containing performance results, uploads it to S3, and updates the SNS notification body to include a link to the doc in S3.
Consolidate existing Quarto docs (in reports/) into a single document
Add simple residuals scatterplots by township
Moran's I / spatial autocorrelation stats
IAAO performance stats by area
Lorenz curves by area
Checking for highly correlated variables, always check residuals plot (calibration plots with probably) cal_plot_regression
This year, we're going to consolidate all diagnostic reporting into a single Quarto document that gets created for each model run. The document will knit at the end of each run, once the model is finished training, has created performance statistics, etc. The output PDF (or HTML) will be uploaded to S3 along with the other model artifacts, then linked in SNS completion notifications.
This Quarto doc will be the primary way we evaluate individual model performance. Cross-model comparison will still be done via Tableau.
model_qc.qmd
might be a good starting place for defining the Quarto doc itself.Tasks
05-finalize.R
step of the model so that it generates a Quarto doc containing performance results, uploads it to S3, and updates the SNS notification body to include a link to the doc in S3.reports/
) into a single documentprobably
)cal_plot_regression
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