Meta Machine Learning (MetaML)

Meta Machine Learning (MetaML) is a Python-based machine learning library built on top of various libraries such as: scikit-learn, fastai, lightGBM, lightning, catboost and others that will be added in the future. This project provides a set of meta machine learning models by unifying the interface of different machine learning libraries based on the Scikit-learn API protocol. The user does not need to know how to install and import those machine learning libraries, and does not need to change the code if they switch the library. Every model is unified into one single efficient API.

Moreover, MetaML provides the verified and efficient machine learning hyper-parameters settings. This can help users better understand the model parameters and better tune the hyper-parameters.

adagrad-classifier all ard-regressor arima-forecaster auto-arima-forecaster auto-ets-forecaster base-reduction-stacking-classifier base-reduction-stacking-regressor bayesian-ridge-regressor catboost-classifier catboost-regressor cd-classifier cd-regressor decision-tree-classifier decision-tree-regressor elastic-net-regressor extra-trees-classifier feature-reduction-stacking-classifier feature-reduction-stacking-regressor fista-classifier fista-regressor gaussian-naivebayes-classifier gaussian-process-classifier gradient-boosting-classifier gradient-boosting-regressor hist-gradient-boosting-classifier hist-gradient-boosting-regressor huber-regressor intelex-elastic-net-regressor intelex-linear-regressor intelex-logistic-regression-classifier intelex-random-forest-classifier intelex-random-forest-regressor intelex-svm-classifier intelex-svr-regressor lasso-regressor lightgbm-classifier lightgbm-regressor linear-discriminant-analysis-classifier linear-regression-forecaster linear-regressor linearsvc-classifier linearsvr-regressor logistic-regression-classifier mean-dummy-regressor median-dummy-regressor naive-drift-forecaster naive-last-forecaster naive-mean-forecaster nbeats-forecaster nearest-centroid-classifier nixtla-arima-forecaster nixtla-auto-arima-forecaster nixtla-auto-ces-forecaster nixtla-auto-ets-forecaster nixtla-auto-theta-forecaster nixtla-garch-forecaster nixtla-historic-average-forecaster nixtla-random-walk-with-drift-forecaster nixtla-seasonal-naive-forecaster nixtla-seasonal-window-average-forecaster nlp-sequence-classifier nlp-sequence-regressor passive-aggressive-classifier passive-aggressive-regressor perceptron-classifier prior-dummy-classifier quadratic-discriminant-analysis-classifier random-forest-classifier random-forest-regressor ridge-regressor saga-classifier sdca-classifier sgd-classifier sgd-regressor stacking-classifier stacking-regressor stratified-dummy-classifier svm-classifier svr-regressor svrg-classifier uniform-dummy-classifier xgboost-classifier xgboost-regressor

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Release files for turintech-evoml-metaml 3.2.2

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File	Interpreter	ABI	Platform	Reset
turintech_evoml_metaml-3.2.2-py3-none-any.whl 449.1 kB Apr 17, 2025	Python 3	none	any	Details

Release files / turintech_evoml_metaml-3.2.2-py3-none-any.whl

Download URL	turintech_evoml_metaml-3.2.2-py3-none-any.whl
Size	449.1 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`72baa9acd9ac2f7f57a1665a74c370aff68520e8d238b35d58cc073c00cbb969`
BLAKE2b-256 checksum How to use checksums	`6c068dbdbe01d9024d7d583f32a15e600412344b92635cb3c25a91d01ccedc0b`
Upload date	Apr 17, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.10.12`