title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¸Ð½ÑÐµÑÐ¿ÑÐµÑÐ°ÑÐ¸Ð¸ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² ÑÑÐ½ÐºÑÐ¸Ð¾Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð·Ð°ÑÐ¾Ñ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: Development and research of an algorithm for interpretation of the results of the functioning of machine learning models with the usage of contamination model
Creators: name: ÐÐ¸ÑÐ½ÑÐºÐ¾Ð², ÐÐ¸ÑÐ¸Ð»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
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
Ð¾Ð±ÑÑÑÐ½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
Ð¼Ð¾Ð´ÐµÐ»Ñ Ð·Ð°ÑÐ¾ÑÐµÐ½Ð¸Ñ
shap
machine learning
artificial intelligence
explainable artificial intelligence
contamination model

description: ÐÐ±ÑÑÑÐ½ÐµÐ½Ð¸Ðµ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¹ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ ÑÐ²Ð»ÑÐµÑÑÑ Ð¾Ð´Ð½Ð¾Ð¹ Ð¸Ð· Ð²Ð°Ð¶Ð½ÐµÐ¹ÑÐ¸Ñ Ð·Ð°Ð´Ð°Ñ Ð² ÑÐ°ÐºÐ¸Ñ Ð¾Ð±Ð»Ð°ÑÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ° ÐºÐ°Ðº Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½Ð°, ÑÐ¸Ð½Ð°Ð½ÑÑ Ð¸ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ Ð°Ð²ÑÐ¾Ð½Ð¾Ð¼Ð½ÑÐ¼Ð¸ ÑÐ¸ÑÑÐµÐ¼Ð°Ð¼Ð¸. ÐÐ´Ð½Ð¸Ð¼ Ð¸Ð· Ð¾ÑÐ½Ð¾Ð²Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±ÑÑÑÐ½Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ° ÑÐ²Ð»ÑÐµÑÑÑ Ð¼ÐµÑÐ¾Ð´ SHAP. ÐÐ±ÑÑÐ½Ð°Ñ Ð²ÐµÑÑÐ¸Ñ SHAP Ð½Ðµ Ð¿ÑÐµÐ´Ð½Ð°Ð·Ð½Ð°ÑÐµÐ½Ð° Ð´Ð»Ñ ÑÐ°Ð±Ð¾ÑÑ Ñ ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸ÑÐ¼Ð¸ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐµÐ¹, ÑÑÐ¾ Ð·Ð°ÑÑÑÐ´Ð½ÑÐµÑ Ð¸Ð½ÑÐµÑÐ¿ÑÐµÑÐ°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹, ÑÐµÑÐ°ÑÑÐ¸Ñ Ð·Ð°Ð´Ð°ÑÑ Ð¼Ð½Ð¾Ð³Ð¾ÐºÐ»Ð°ÑÑÐ¾Ð²Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. ÐÑÐµ Ð¾Ð´Ð½Ð¸Ð¼ Ð½ÐµÐ´Ð¾ÑÑÐ°ÑÐºÐ¾Ð¼ ÑÑÐ°Ð½Ð´Ð°ÑÑÐ½Ð¾Ð³Ð¾ SHAP ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ¾, ÑÑÐ¾ Ð¾Ð½ Ð½Ðµ ÑÑÐ¸ÑÑÐ²Ð°ÐµÑ Ð½ÐµÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¸ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¸ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ ÑÐ¸ÑÐµÐ» Ð¨ÐµÐ¿Ð»Ð¸. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¾Ð¿Ð¸ÑÐ°Ð½ ÑÐ¿Ð¾ÑÐ¾Ð± ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¼ÐµÑÐ¾Ð´Ð° SHAP Ð½Ð° ÑÐ»ÑÑÐ°Ð¹ Ð¼Ð½Ð¾Ð³Ð¾ÐºÐ»Ð°ÑÑÐ¾Ð²Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸, Ð² ÐºÐ¾ÑÐ¾ÑÐ¾Ð¼ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÑÑÑÑ Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²Ð° ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ð¹ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐµÐ¹, Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð½ÑÑ Ð² ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²Ð¸Ð¸ ÑÐ¾ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»ÑÑ ÑÐ¿ÑÐ¸Ð»Ð¾Ð½-Ð·Ð°ÑÐ¾ÑÐµÐ½Ð¸Ñ, ÑÑÐ¾ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð¿Ð¾Ð»ÑÑÐ¸ÑÑ Ð¸Ð½ÑÐµÑÐ²Ð°Ð»Ñ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ ÑÐ¸ÑÐµÐ» Ð¨ÐµÐ¿Ð»Ð¸ Ð²Ð¼ÐµÑÑÐ¾ Ð¾Ð´Ð¸Ð½Ð¾ÑÐ½ÑÑ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹. ÐÐ¾ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð², Ð±ÑÐ»Ð¾ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð½Ð¾, ÑÑÐ¾ Ð¾Ð´Ð¸Ð½Ð¾ÑÐ½ÑÐµ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ ÑÐ¸ÑÐµÐ» Ð¨ÐµÐ¿Ð»Ð¸ Ð¸Ð·-Ð·Ð° Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾Ð¹ Ð½ÐµÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ Ð²Ð¾Ð·Ð½Ð¸ÐºÐ°ÑÑÐµÐ¹ Ð½ÐµÐ¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸ Ð¸Ñ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¸, ÑÐ°ÑÑÐ¾ Ð²ÑÐ´Ð°ÑÑ Ð½ÐµÐ½Ð°Ð´ÐµÐ¶Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ, Ð¸ Ð²Ð¼ÐµÑÑÐ¾ Ð½Ð¸Ñ Ð´Ð¾Ð»Ð¶Ð½Ñ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÑÑÑÑ Ð¸Ð½ÑÐµÑÐ²Ð°Ð»Ñ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹.
description: Explanation of the predictions of machine learning models is one of the most important tasks in such areas of artificial intelligence applications as medicine, finance, and control of autonomous systems. One of the main methods of explainable artificial intelligence is the SHAP method. The regular version of the SHAP is not intended to work with probability distributions, which makes it difficult to interpret models that solve the problem of multiclass classification. Another disadvantage of the standard SHAP is that it does not take into an account the imprecision of the modelâs predictions when calculating the values of the Shapley numbers. This work describes a method for implementing the SHAP algorithm for the case of multiclass classification problem, which considers sets of distributions constructed in accordance with the statistical epsilon-contamination model, which allows obtaining intervals of Shapley numbers instead of single values. The experiments are presented showing that single values of the Shapley numbers often give unreliable results, due to the possible inaccuracy of the model's predictions and the resulting uncertainty in their calculation, and instead of them, intervals of values should be considered.