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WindcleaverDevWindcleaverDev/regkit/.agents/skills/logistic-regression/SKILL.md

logistic-regression

Skill0 starsChanged 5 months agoMITView on GitHub

Fits a binary logistic regression with statsmodels, produces a LogisticRegressionReport validating against the pack schema, renders a standalone HTML report. Outputs results/report.json (LogisticRegressionReport) and results/report.html. Lead with marginal effects (AME), not raw log-odds. AME tells you the average change in predicted probability per unit increase in each feature — the quantity humans find most interpretable. After fit, run diagnostics for assumption checks adapted to logistic models (log-odds linearity, leverage analysis). Or run model-comparison alongside an OLS fit on the same…

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    After fit, run diagnostics for assumption checks adapted to logistic models (log-odds linearity, leverage analysis).

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SKILL.md

logistic-regression

Fits a binary logistic regression with statsmodels, produces a LogisticRegressionReport validating against the pack schema, renders a standalone HTML report.

When to use

  • Target has exactly two distinct values (0/1, yes/no, true/false, two category names)
  • User asks for logistic regression, "model a binary outcome", "predict yes/no"
  • pre-analysis identified the target as binary
  • User wants odds ratios or predicted probabilities, not just coefficient signs

Quick start

python .agents/skills/logistic-regression/scripts/fit.py \
    --data path/to/data.csv \
    --target churned \
    --positive-class yes \
    --features tenure,monthly_charges,contract_type \
    --output results/

Outputs results/report.json (LogisticRegressionReport) and results/report.html.

Arguments

FlagDefaultDescription
--datarequiredCSV or Parquet path
--targetrequiredBinary target column (exactly 2 unique values)
--positive-classautoWhich value to treat as 1 (default: 1 if 0/1 numeric, else alphabetically last)
--featuresrequiredComma-separated columns, or all
--outputrequiredOutput directory
--robust-seoffHC0–HC3 robust standard errors
--threshold0.5Classification decision threshold
--dataset-name""Label shown in report header

Outputs

out/
├── report.json   # LogisticRegressionReport (Pydantic schema)
└── report.html   # Self-contained HTML with Plotly charts

Report sections

  1. Fit summary — AUC, Brier score, McFadden pseudo-R², AIC/BIC, positive rate
  2. Marginal effects — AME forest plot + table (the primary interpretation surface)
  3. Coefficients — log-odds table + odds ratio forest plot (log scale)
  4. Classification performance — confusion matrix, accuracy, balanced accuracy, F1
  5. ROC curve — AUC annotated
  6. Calibration — reliability diagram with Brier score
  7. Interpretation — AME-based plain-English facts per feature
  8. Flags & recommendations

Interpreting the output

Lead with marginal effects (AME), not raw log-odds. AME tells you the average change in predicted probability per unit increase in each feature — the quantity humans find most interpretable.

  • AUC 0.5 = random, 0.7 = OK, 0.8 = good, 0.9+ = strong
  • Brier score 0.25 = baseline for balanced classes (lower is better)
  • CLASS_IMBALANCE flag: accuracy is misleading; prefer balanced accuracy and AUC

Chaining

After fit, run diagnostics for assumption checks adapted to logistic models (log-odds linearity, leverage analysis). Or run model-comparison alongside an OLS fit on the same binary target.

References

  • references/odds_ratio_interpretation.md
  • references/marginal_effects.md
  • references/class_imbalance.md
  • references/threshold_choice.md

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