parleml-ml4

ml4 is the training engine of ParleML, the machine learning platform built to be driven by AI agents. It turns a table into a deployable model: feature engineering, training on AutoGluon, then calibration, thresholds and honesty checks. Free to use, including commercial use.

pip install parleml-ml4

import parleml_ml4

cfg = parleml_ml4.Ml4Config()
cfg.data.target_column = "churned"                     # the column you want to predict
model = parleml_ml4.AutoSupervised(cfg).fit(train_df)  # plan features, train, calibrate
risk = model.predict_proba(new_customers)              # one column per class
model.save("out/churn")

fit returns a FittedPipeline: the trained model plus everything it needs to score new rows, with the scikit-learn predict / predict_proba interface.

One entry point per kind of work

What it adds to AutoGluon

• Leakage-safe feature engineering. Domain parsers (IP and geography, emails, names, user agents, addresses, postal codes, phone, card, IBAN and VAT numbers, dates), target encodings, interactions and text encoders. Target-aware features are refitted inside every fold.
• A post-fit layer. Calibration, decision-threshold tuning, prediction intervals and a check against a constant baseline, refittable without retraining.
• Regulated models. Glass-box models (GLM, scorecard, RuleFit, EBM), derived monotonic constraints, and presets such as regulated_glassbox and regulated_monotonic.
• Forecasting with four backends. Statistical, tree, the Chronos foundation model and AutoGluon-TimeSeries, with per-series validation and conformal intervals.
• Hyperparameter search with one owner you choose: ml4's Optuna search, AutoGluon's, or good defaults with no search.

Its partner parleml-mli explains the models ml4 builds.

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. The core includes AutoGluon Tabular, PyTorch, the tree boosters, the glass-box models, association mining and anomaly detection. Add extras for the rest:

pip install "parleml-ml4[forecasting,forecasting-autogluon]"

PyTorch comes from PyPI: the CPU build on Windows, the CUDA build on Linux. For a CPU-only Linux install, run pip install torch --index-url https://download.pytorch.org/whl/cpu first.

IP geolocation reads MaxMind GeoLite2 databases, which you download yourself and point MAXMIND_DB_DIR at; they are not shipped, for licensing reasons.

Documentation

• The ml4 documentation: installation, quickstart, guides, every configuration parameter and the API reference.
• ParleML and its manual: the platform, and using its models from Python. 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.