Introduction 简介 You’ve seen (and used) techniques to extract general insights from a machine learning model. But what if you want to break down how the model works for an individual prediction? 您已经看到（并使用）了从机器学习模型中提取一般见解的技术。但是，如果您想分解模型如何针对单个预测发挥作用，该怎么办？ SHAP Values (an acronym from SHapley Additive exPlanations) break down a prediction to show the impact of each feature. Where could you […]

This notebook is an exercise in the Machine Learning Explainability course. You can reference the tutorial at this link. Set Up 设置 Today you will create partial dependence plots and practice building insights with data from the Taxi Fare Prediction competition. 今天，您将创建部分依赖关系图，并练习使用来自 出租车费预测 竞赛的数据来阐述问题。 We have again provided code to do the basic loading, review […]

Partial Dependence Plots 部分依赖图 While feature importance shows what variables most affect predictions, partial dependence plots show how a feature affects predictions. 虽然特征重要性显示了哪些变量对预测影响最大，但部分依赖图显示了特征如何影响预测。 This is useful to answer questions like: 这对于回答以下问题很有用： Controlling for all other house features, what impact do longitude and latitude have on home prices? To restate this, how would similarly sized houses […]

This notebook is an exercise in the Machine Learning Explainability course. You can reference the tutorial at this link. Intro 简介 You will think about and calculate permutation importance with a sample of data from the Taxi Fare Prediction competition. 您将使用来自 出租车费预测 竞赛的数据样本来思考和计算排列重要性。 We won’t focus on data exploration or model building for now. You […]

Introduction 简介 One of the most basic questions we might ask of a model is: What features have the biggest impact on predictions? 我们可能会问模型的一个最基本问题是：哪些特征对预测的影响最大？ This concept is called feature importance. 这个概念称为特征重要性。 There are multiple ways to measure feature importance. Some approaches answer subtly different versions of the question above. Other approaches have documented shortcomings. 有多种方法来衡量特征重要性。一些方法回答了上述问题的微妙不同版本。其他方法有记录的缺点。 […]

What Types of Insights Are Possible 可能获得哪些类型的洞察 Many people say machine learning models are "black boxes", in the sense that they can make good predictions but you can’t understand the logic behind those predictions. This statement is true in the sense that most data scientists don’t know how to extract insights from models yet. 许多人说机器学习模型是“黑匣子”，即它们可以做出正确的预测，但你无法理解这些预测背后的逻辑。这种说法是正确的，因为大多数数据科学家还不知道如何从模型中提取洞察。 […]