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📉 Почему добавление новых данных иногда не улучшает качество модели

Если метрики перестают расти, возможны несколько причин:

➡️ Модель достигла предела своей емкости — она просто не может выучить больше, чем уже выучила.

➡️ Новые данные не добавляют полезной информации — они слишком похожи на то, что уже есть.

➡️ Данные содержат шум или ошибки в метках — добавление «плохих» примеров только ухудшает обучение.

➡️ Недостаточно мощная архитектура — текущая модель не способна уловить сложные зависимости.

В таких случаях лучше поработать над архитектурой, качеством данных или настройкой гиперпараметров, а не просто расширять выборку.

Библиотека собеса по Data Science

Библиотека собеса по Data Science | вопросы с собеседований
📉 Почему добавление новых данных иногда не улучшает качество модели

Если метрики перестают расти, возможны несколько причин:

➡️ Модель достигла предела своей емкости — она просто не может выучить больше, чем уже выучила.

➡️ Новые данные не добавляют полезной информации — они слишком похожи на то, что уже есть.

➡️ Данные содержат шум или ошибки в метках — добавление «плохих» примеров только ухудшает обучение.

➡️ Недостаточно мощная архитектура — текущая модель не способна уловить сложные зависимости.

В таких случаях лучше поработать над архитектурой, качеством данных или настройкой гиперпараметров, а не просто расширять выборку.

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📉 Почему добавление новых данных иногда не улучшает качество модели

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