---

name: statsmodels-logit-workflow description: "Fit the admissions logistic regression example with pandas and statsmodels, using bundled CSV fixtures, schema validation, prestige dummy encoding, intercept handling, prediction CSV output, and optional non-interactive plots." disable-model-invocation: true metadata: disco-role: operating license: MIT

statsmodels-logit-workflow

Use this sub-skill for the admissions logistic-regression example built with pandas and statsmodels.

Route here for

• Loading the bundled admissions train/test CSV fixtures.
• Validating the required schema and normalizing prestige values.
• Fitting statsmodels.api.Logit with an intercept and aligned dummy columns.
• Writing a prediction CSV with probability and thresholded labels.
• Generating optional PNG diagnostics without an interactive display.

Route away

• scikit-learn or Kaggle-style classifier pipelines: ../kaggle-linear-models/SKILL.md
• Tutorial/resource navigation or Python snippet cleanup: ../tutorial-resource-map/SKILL.md

Start here

• End-to-end recipe: references/workflows.md
• Column schema and output layout: references/data-formats.md
• Failure diagnosis: references/troubleshooting.md
• Bundled helper: scripts/statsmodels_admission_logit.py

Bundled helper

python3 scripts/statsmodels_admission_logit.py \
  --train references/data/admissions_train.csv \
  --test references/data/admissions_test.csv \
  --output ./admissions_predictions.csv \
  --no-plots