CLCODING
Фотография
нажмите — покажем
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 ·