Statistical ML Skill

Overview

statistical-ml is the shared NeuroClaw implementation for classical prediction and inference on subject-level tabular features. It keeps imputation, scaling, feature selection, and model fitting inside each training fold.

Supported estimators

Task	Models
Classification	logistic, ridge, elastic_net, svm, xgboost
Regression	ols, ridge, elastic_net, svr, xgboost
Inference	Cohen's d, robust formula OLS/GLM, linear mixed-effects models
Longitudinal treatment analysis	dose/time/treatment mixed-effects formulas

Use this skill after imaging data have been converted into one row per subject or observation. Use temporal-models for sequence tensors and cnn3d for full 3D volumes.

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Installation

Core dependencies are installed with NeuroClaw:

pip install numpy pandas scipy scikit-learn statsmodels joblib

XGBoost is optional:

pip install xgboost

Verify imports:

python -c "import sklearn, statsmodels; print('Statistical ML OK')"

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Workflows

1. Prepare a tabular CSV

The CSV must contain a subject identifier, target, and numeric features:

subject_id,site,diagnosis,age,roi_001,roi_002,network_fc
sub-001,A,0,24,0.12,-0.04,0.31
sub-002,B,1,31,0.08,-0.09,0.27

Identifier, target, and optional group columns are excluded from predictors.

2. Classification

python skills/statistical-ml/scripts/train_reference.py \
  --features features.csv \
  --target diagnosis \
  --subject-col subject_id \
  --group-col site \
  --model logistic \
  --task classification \
  --folds 5 \
  --output-dir run_models_output/logistic

Use --group-col site or another cohort column when sites must not be split between training and test folds.

3. Regression

python skills/statistical-ml/scripts/train_reference.py \
  --features features.csv \
  --target cognitive_score \
  --model ridge \
  --task regression \
  --folds 5 \
  --output-dir run_models_output/ridge

4. Dry-run schema validation

python skills/statistical-ml/scripts/train_reference.py \
  --features features.csv \
  --target diagnosis \
  --model svm \
  --task classification \
  --output-dir run_models_output/check \
  --dry-run

5. Formula-based inference

Use the Python APIs in models/statistical_ml/mixed_effects.py for robust OLS, mixed-effects, and dose-response analyses. Report the formula, grouping variable, effect estimate, confidence interval, and multiplicity correction.

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Input / Output Summary

Item	Format
Input	CSV; one row per subject or observation
Required columns	subject ID, target, numeric features
Optional column	grouping/site column
Predictions	predictions.csv
Fold membership	fold_assignments.csv
Metrics and config	metrics.json, config.json
Fitted estimators	checkpoint.joblib
Provenance	run_manifest.json

Classification reports AUROC, AUPRC, accuracy, and balanced accuracy where defined. Regression reports MAE, RMSE, and correlation metrics.

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Testing

pytest models/tests/test_extended_models.py -q
python skills/statistical-ml/scripts/train_reference.py --help

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Directory Reference

models/statistical_ml/
├── estimators.py       estimator factory and preprocessing pipelines
├── mixed_effects.py    OLS, effect-size, and mixed-effects utilities
└── train.py            cross-validated CLI

skills/statistical-ml/
├── SKILL.md
└── scripts/train_reference.py

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Reference

• Scikit-learn pipelines are used to prevent preprocessing leakage.
• Statsmodels provides formula-based OLS/GLM and mixed-effects inference.
• XGBoost remains optional and is loaded only when requested.

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Created At: 2026-07-26 HKT Last Updated At: 2026-07-29 HKT Author: chengwang96