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parleml-mli

mli is the interpretation library of ParleML, the machine learning platform built to be driven by AI agents. It explains fitted models and the data behind them: who the model flags and why, which rules it learned, how good and how trustworthy it is. Free to use, including commercial use.

pip install parleml-mli
from parleml_mli import ExplanationsConfig, ProfilingConfig, RulesConfig, fit_profiler

result = fit_profiler(
    model=clf, X=X_train, y=y_train,
    target_mask=(y_train == 1),             # the rows to profile: here, the positives
    X_test=X_test, y_test=y_test,           # held-out rows for the model evaluation
    X_holdout=X_test, y_holdout=y_test,     # held-out rows for the rules' Test numbers
    config=ProfilingConfig(rules=RulesConfig(), explanations=ExplanationsConfig()),
)
result.summary_df   # one row per archetype: size, lift, key signatures
result.save("out/profiling_result.joblib")

fit_profiler returns a ProfilingResult with the analyses you asked for. For a scorer you can't open, or for stored predictions, use monitor_predictions.

What it computes

Capability Entry point What you get
Behavioural archetypes fit_profiler Clusters of the target rows in tree-leaf or SHAP space, with signatures and similar cases
Rule extraction extract_rules IF-THEN rules from supported trees, with Train and, given held-out rows, Test metrics
Global explanations explain PDP, ALE, interactions, surrogate, importance, drift, fairness and the error tree
EDA and leakage compute_eda Distributions, associations, missing values, outliers and suspicious features; no model needed
Evaluation and label noise parleml_mli.evaluation Metrics, calibration, business-cost views, and rows worth a second look
Trust summary compute_trust_summary Diagnostic tiles from the evidence you have; missing evidence never shows as a pass
Model cards build_model_card A structured model card with regulatory crosswalks
Bring-your-own predictions monitor_predictions The model-agnostic analyses from a scorer you can't open

It reads models from parleml-ml4, which it installs, and models trained elsewhere. LightGBM, XGBoost, CatBoost, scikit-learn tree ensembles, EBM, RuleFit and scikit-learn linear models get the full analysis; any other model with predict or predict_proba gets the model-agnostic one through monitor_predictions.

Installation

Python 3.11, 3.12 or 3.13 on Windows or Linux (x86-64). The wheels are compiled, so pip picks the one for your platform; on macOS, run ParleML in Docker. Tree plots need the Graphviz dot program on your PATH (a system package, not a Python one).

Documentation

  • The mli documentation: installation, quickstart, guides and the API reference.
  • ParleML and its manual: the platform, and the Interpretation tabs built on mli. The same documentation ships with ParleML under /help.

Questions: support@parleml.com.

Licence

Free to use, including commercial use, under the ParleML terms of use (LICENSE.txt in this package). Third-party material this package contains is listed in NOTICE.txt; every ParleML notice is at parleml.com/legal.

Exported scoring packages may be sold and distributed with the required ParleML runtime unchanged. Selling ParleML or charging for access to its platform is not permitted.

Metadata

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