商品描述
Most intermediate-level machine learning books usually focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance and the need to be able to explain why and how your ML model makes the predictions that it does.
This practical guide brings together the best-in-class techniques for model interpretability and explains model predictions in a hands-on approach. Experienced ML practitioners will be able to more easily apply these tools in their daily workflow.
商品描述(中文翻譯)
大多數中級機器學習書籍通常著重於如何通過提高準確性或降低預測誤差來優化模型。但這種方法往往忽視了能夠解釋為什麼以及如何進行預測的重要性和必要性。
這本實用指南匯集了最佳的模型可解釋性技術，並以實踐的方式解釋模型的預測。有經驗的機器學習從業者將能夠更輕鬆地將這些工具應用於他們的日常工作流程中。
