Logistic regression

Skill WindcleaverDev/regkit/.agents/skills/logistic-regression

Statistically rigorous regression skills for Claude — Python computes, the model narrates.

Install

npx -y skills add WindcleaverDev/regkit --skill logistic-regression

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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SKILL.md

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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

Flag	Default	Description
`--data`	required	CSV or Parquet path
`--target`	required	Binary target column (exactly 2 unique values)
`--positive-class`	auto	Which value to treat as 1 (default: `1` if 0/1 numeric, else alphabetically last)
`--features`	required	Comma-separated columns, or `all`
`--output`	required	Output directory
`--robust-se`	off	`HC0`–`HC3` robust standard errors
`--threshold`	`0.5`	Classification 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

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