title: ÐÐ½Ð°Ð»Ð¸Ð· Ð¸ÑÑ Ð¾Ð´Ð½Ð¾Ð³Ð¾ ÐºÐ¾Ð´Ð° Ð´Ð»Ñ Ð¿Ð¾Ð¸ÑÐºÐ° Ð¸ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Analysis of source code for search and optimization of hyperparameters of machine learning models
Creators: name: ÐÐ½Ð¸ÑÑÐºÐ¾Ð², ÐÐ¸ÐºÐ¸ÑÐ°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¼ÐµÑÐ°-Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð½Ð° Ð¸ÑÑ Ð¾Ð´Ð½Ð¾Ð¼ ÐºÐ¾Ð´Ðµ
machine learning
meta-learning
automated machine learning
machine learning on source code

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description: The subject of this work is âAnalysis of source code for search and optimization of hyperparameters of machine learning modelsâ. This study solves the problem of metaÂ­learning on the source code to optimize the hyperparameters of machine learning models; the proposed method for constructing a metaÂ­algorithm that can extract meta Â­information about a machine learning problem from the source code of its solution and use the extracted metaÂ­information to recommend hyperparameters for machine learning models; a testing system for the proposed metaÂ­ algorithm is developed and implemented. During the elaboration of the problem, the following tasks were completed: 1. Collecting a dataset from Kaggle platform. 2. The initial analysis and preprocessing of the data collected from Kaggle and the available data from GitHub was carried out. 3. Developing and implementing a testing system for metaÂ­algorithms and hyperparameters using the source code as a source of information about a machine learning task. 4. ThearchitectureofthehyperparameterrecommendationmetaÂ­algorithmisproposed, which uses the construction of vector representations of the code using CodeBERT and the recommendation using the random forest model. 5. The proposed architecture of the metaÂ­algorithm is evaluated using a testing system. This study presents a prototype of a new approach to solving the problem of metaÂ­ learning â the use of metaÂ­algorithm information about the source code that solves the problem of machine learning. Our solution allows us to evaluate any metaÂ­algorithms for hyperparameter recommendations that use the source code as a source of metaÂ­ information for a given data sample of a machine learning problem and an arbitrary selection of Python codes that solve this problem. The main result of the experiments is a demonstration of the developed and implemented system for testing metaÂ­algorithms that predict the hyperparameters of machine learning models from the source code. The testing system allows you to compare the quality of various hyperparameter selection strategies for a given data set of a machine learning problem and a set of source codes that solve it.