In this 1-hour long project-based course, you will learn how to create interpretable machine learning applications on the example of two classification regression models, decision tree and random forestc classifiers. You will also learn how to explain such prediction models by extracting the most important features and their values, which mostly impact these prediction models. In this sense, the project will boost your career as Machine Learning (ML) developer and modeler in that you will be able to get a deeper insight into the behaviour of your ML model. The project will also benefit your career as a decision maker in an executive position, or consultant, interested in deploying trusted and accountable ML applications.


您将学到什么
How to select and compare different prediction models (classification regressors) for a real world dataset (FIFA 2018 Soccer World Cup Statistics).
How to extract the most important features, which impact the classifiers, in a model-agnostic approach, together with caveats.
How to get an insight into the way values of the most important features impact the predictions made by the classifiers.
您将练习的技能
要了解的详细信息

添加到您的领英档案
仅桌面可用
了解顶级公司的员工如何掌握热门技能

在不到 2 个小时的时间内学习、练习和应用为就业做好准备的技能
- 接受行业专家的培训
- 获得解决实训工作任务的实践经验
- 使用最新的工具和技术来建立信心

关于此指导项目
分步进行学习
在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:
Setting the stage (Python Jupyter Lab web-based Server environment, importing the dataset and file to train and test the designated classification regressors as prediction models).
Train, test and estimate the accuracy (confusion matrix) of a Decision Tree classifier.
Train, test and estimate the accuracy (confusion matrix) of a Random Tree classifier as an alternative to the previous one.
Extract a ranking list of the features, which are most important for each one of our prediction models.
Extract and plot the impact of the values of selected important features on predictions being made by each one of our prediction models.
推荐体验
Molnar, C.: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, https://christophm.github.io/interpretable-ml-book/
2个项目图片
位教师

学习方式
基于技能的实践学习
通过完成与工作相关的任务来练习新技能。
专家指导
使用独特的并排界面,按照预先录制的专家视频操作。
无需下载或安装
在预配置的云工作空间中访问所需的工具和资源。
仅在台式计算机上可用
此指导项目专为具有可靠互联网连接的笔记本电脑或台式计算机而设计,而不是移动设备。
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学生评论
60 条评论
- 5 stars
55%
- 4 stars
35%
- 3 stars
5%
- 2 stars
1.66%
- 1 star
3.33%
显示 3/60 个
已于 Aug 6, 2022审阅
Pretty Informative and crisp to the point. Great hands on course.
已于 Sep 25, 2025审阅
The pdp library did not match the project requirements
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常见问题
购买指导项目后,您将获得完成指导项目所需的一切,包括通过 Web 浏览器访问云桌面工作空间,工作空间中包含您需要了解的文件和软件,以及特定领域的专家提供的分步视频说明。
由于您的工作空间包含适合笔记本电脑或台式计算机使用的云桌面,因此指导项目不在移动设备上提供。
指导项目授课教师是特定领域的专家,他们在项目的技能、工具或领域方面经验丰富,并且热衷于分享自己的知识以影响全球数百万的学生。