Ended 3 months ago
40 participants
131 submissions

Solution examples:

  • LightAutoML (v. 0.2.16)
    LightAutoML (LAMA) - project from Sberbank AI Lab AutoML group is the framework for automatic classification and regression model creation.
  • AutoSklearn (v. 0.13.0)
    auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
  • H2O (v.
    AutoML is a function in H2O that automates the process of building a large number of models, with the goal of finding the "best" model without any prior knowledge or effort by the Data Scientist.
  • Linear_Regression (scikit-learn 1.0.1)
    LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.
  • MLJar (mljar-supervised 0.11.1)
    The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameters tuning to find the best model trophy. It is no black-box as you can see exactly how the ML pipeline is constructed (with a detailed Markdown report for each ML model).
  • Random_Forest (scikit-learn 1.0)
    A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.
  • Tpot (v. 0.11.7)
    TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
  • AutoGluon (v. 0.3.1)
    AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on text, image, and tabular data.

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