Веб-версияОткрыть в Telegram

ПостSHAP makes machine learning models easier to understand.

5 октября 2026
C
CLCODING
Фотография
нажмите — покажем
SHAP makes machine learning models easier to understand. Instead of just getting a prediction, you can see why the model made that prediction and which features pushed the result higher or lower. In this example, a Random Forest model predicts a value from the California housing dataset, while a SHAP waterfall plot breaks down the individual contribution of each feature. For example: - AveOccup pushes the prediction down - MedInc pushes it up - Other features such as Longitude, Latitude, Population, and HouseAge also influence the final prediction This is the power of Explainable AI (XAI) — moving from “What did the model predict?” to “Why did the model predict it?” Projects: https://link.amazon/B03Rr0s1k
1 · 55 ·

Рядом в ленте

CCLCODINGPrime deals are live, and if you're a developer, this can be a good opportunity to upgrade your workspace, coding setup, and everyday tech accessories. You don'CCLCODINGUnderstanding Machine Learning: From Theory to Algorithms — Free PDF Looking to build a strong foundation in Machine Learning? This 449-page book takes you beyo
это сообщение
PPython Coding (CLCODING)Python Coding (CLCODING)@pythonclcoding · канал · Технологии
9 625подписчиков311средний охват поста
Лента площадки Открыть в Telegram

Открытая публичная лента из поискового индекса ChatCrawler — «Google по публичному Telegram»; обновляется по мере обхода площадки. Время — UTC.

Только публичный контент, официальный API Telegram. О проекте · Вопросы · Чего мы не делаем · Убрать страницу из выдачи · Каталог · Поиск · Как мы считаем