Set of meta machine learning models by unifying the interface of different machine learning libraries based on the Scikit-learn API protocol

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  • License: TURING INTELLIGENCE TECHNOLOGY LIMITED END-USER LICENCE
  • Author: Turing Intelligence Technology
  • Tags machine , learning
  • Requires: Python <4, >=3.9
  • Provides-Extra: catboost-classifier , nlp-sequence-classifier , intelex-logistic-regression-classifier , intelex-random-forest-classifier , intelex-svm-classifier , lightgbm-classifier , adagrad-classifier , cd-classifier , fista-classifier , saga-classifier , sdca-classifier , svrg-classifier , decision-tree-classifier , extra-trees-classifier , gaussian-naivebayes-classifier , gaussian-process-classifier , gradient-boosting-classifier , hist-gradient-boosting-classifier , linear-discriminant-analysis-classifier , linearsvc-classifier , logistic-regression-classifier , nearest-centroid-classifier , passive-aggressive-classifier , perceptron-classifier , prior-dummy-classifier , quadratic-discriminant-analysis-classifier , random-forest-classifier , sgd-classifier , stratified-dummy-classifier , svm-classifier , uniform-dummy-classifier , base-reduction-stacking-classifier , feature-reduction-stacking-classifier , stacking-classifier , xgboost-classifier , linear-regression-forecaster , nbeats-forecaster , 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 , arima-forecaster , auto-arima-forecaster , auto-ets-forecaster , naive-drift-forecaster , naive-last-forecaster , naive-mean-forecaster , catboost-regressor , nlp-sequence-regressor , intelex-elastic-net-regressor , intelex-linear-regressor , intelex-random-forest-regressor , intelex-svr-regressor , lightgbm-regressor , cd-regressor , fista-regressor , ard-regressor , bayesian-ridge-regressor , decision-tree-regressor , elastic-net-regressor , gradient-boosting-regressor , hist-gradient-boosting-regressor , huber-regressor , lasso-regressor , linear-regressor , linearsvr-regressor , mean-dummy-regressor , median-dummy-regressor , passive-aggressive-regressor , random-forest-regressor , ridge-regressor , sgd-regressor , svr-regressor , base-reduction-stacking-regressor , feature-reduction-stacking-regressor , stacking-regressor , xgboost-regressor , all
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Project description

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.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for turintech from gravatar.com turintech

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: TURING INTELLIGENCE TECHNOLOGY LIMITED END-USER LICENCE
  • Author: Turing Intelligence Technology
  • Tags machine , learning
  • Requires: Python <4, >=3.9
  • Provides-Extra: catboost-classifier , nlp-sequence-classifier , intelex-logistic-regression-classifier , intelex-random-forest-classifier , intelex-svm-classifier , lightgbm-classifier , adagrad-classifier , cd-classifier , fista-classifier , saga-classifier , sdca-classifier , svrg-classifier , decision-tree-classifier , extra-trees-classifier , gaussian-naivebayes-classifier , gaussian-process-classifier , gradient-boosting-classifier , hist-gradient-boosting-classifier , linear-discriminant-analysis-classifier , linearsvc-classifier , logistic-regression-classifier , nearest-centroid-classifier , passive-aggressive-classifier , perceptron-classifier , prior-dummy-classifier , quadratic-discriminant-analysis-classifier , random-forest-classifier , sgd-classifier , stratified-dummy-classifier , svm-classifier , uniform-dummy-classifier , base-reduction-stacking-classifier , feature-reduction-stacking-classifier , stacking-classifier , xgboost-classifier , linear-regression-forecaster , nbeats-forecaster , 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 , arima-forecaster , auto-arima-forecaster , auto-ets-forecaster , naive-drift-forecaster , naive-last-forecaster , naive-mean-forecaster , catboost-regressor , nlp-sequence-regressor , intelex-elastic-net-regressor , intelex-linear-regressor , intelex-random-forest-regressor , intelex-svr-regressor , lightgbm-regressor , cd-regressor , fista-regressor , ard-regressor , bayesian-ridge-regressor , decision-tree-regressor , elastic-net-regressor , gradient-boosting-regressor , hist-gradient-boosting-regressor , huber-regressor , lasso-regressor , linear-regressor , linearsvr-regressor , mean-dummy-regressor , median-dummy-regressor , passive-aggressive-regressor , random-forest-regressor , ridge-regressor , sgd-regressor , svr-regressor , base-reduction-stacking-regressor , feature-reduction-stacking-regressor , stacking-regressor , xgboost-regressor , all
Classifiers

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3.2.2

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3.2.1

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