explainerdashboard允许您快速构建交互式仪表板，以解释机器学习模型的内部工作原理。
explainerdashboard的Python项目详细描述
此包使快速部署仪表板web应用程序变得非常方便 这就解释了（scikit学习兼容）装配机器的工作原理 学习模式。仪表板提供关于模型性能的交互式绘图， 特征重要性，特征对个体预测的贡献， 部分依赖图，形状（交互作用）值，个体形象化 决策树等
目标有很多种：
- Make it easy for data scientists to quickly inspect the inner workings and
- performance of their model with just a few lines of code
- Make it possible for non data scientist stakeholders such as co-workers,
- managers, directors, internal and external watchdogs to interactively inspect the inner workings of the model without having to depend on a data scientist to generate every plot and table
- Make it easy to build a custom application that explains individual
- predictions of your model for customers that ask for an explanation
- Explain the inner workings of the model to the people working with
- model in a human-in-the-loop deployment so that they gain understanding what the model does do and does not do. This is important so that they can gain an intuition for when the model is likely missing information and may have to be overruled.
仪表板包括：
- SHAP values (i.e. what is the contribution of each feature to each
- individual prediction?)
- Permutation importances (how much does the model metric deteriorate
- when you shuffle a feature?)
- Partial dependence plots (how does the model prediction change when
- you vary a single feature?
- Shap interaction values (decompose the shap value into a direct effect
- an interaction effects)
- For Random Forests and xgboost models: visualization of individual trees
- in the ensemble.
- Plus for classifiers: precision plots, confusion matrix, ROC AUC plot,
- PR AUC plot, etc
- For regression models: goodness-of-fit plots, residual plots, etc.
该库的设计是模块化的，因此很容易设计 拥有自定义仪表板，以便您可以专注于布局和项目特定 仪表板的文本说明。（即设计它 可为组织中的业务用户解释，而不仅仅是数据科学家）
可以在http://titanicexplainer.herokuapp.com找到已部署的示例
- 项目
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