Interpretable machine learning applications: Part 3

提供方
在此指导项目中,您将:

Import, explore and normalize real world data (HELOC) for evaluating the risk performance of mortgage applications

Train and test a prediction model as a Sequential model based Artificial Neural Network (ANN)

Generate explanations based on profiles of mortgage applicants closest to the individual requesting the explanation.

2 hours
中级
无需下载
分屏视频
英语(English)
仅限桌面

In this 50 minutes long project-based course, you will learn how to apply a specific explanation technique and algorithm for predictions (classifications) being made by inherently complex machine learning models such as artificial neural networks. The explanation technique and algorithm is based on the retrieval of similar cases with those individuals for which we wish to provide explanations. Since this explanation technique is model agnostic and treats the predictions model as a 'black-box', the guided project can be useful for decision makers within business environments, e.g., loan officers at a bank, and public organizations interested in using trusted machine learning applications for automating, or informing, decision making processes. The main learning objectives are as follows: Learning objective 1: You will be able to define, train and evaluate an artificial neural network (Sequential model) based classifier  by using keras as API for TensorFlow. The pediction model will be trained and tested with the HELOC dataset for approved and rejected mortgage applications. Learning objective 2: You will be able to generate explanations based on similar profiles for a mortgage applicant predicted either as of "Good" or "Bad" risk performance. Learning objective 3: you will be able to generate contrastive explanations based on feature and pertinent negative values, i.e., what an applicant should change in order to turn a "rejected" application to an "approved" one.

您要培养的技能

  • Training and testing an Artificial Neural Network

  • Using the Protodash algorithm

  • Using keras as API for TensorFlow

  • Normalization of data prior to training a prediction model

  • Explanations based on similarity measurements

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:

  1. By the end of task 1, you will be able, as a data scientist or loan officer persona, to load, process and normalize the (HELOC) dataset about mortgage applications for training purposes.

  2. By the end of task 2, you will be able to define, train and evaluate an artificial neural network based classifier  by using TensorFlow.

  3. By the end of tasks 3 and 4, you will be able to obtain similar samples as explanations for a mortgage applicant predicted as "Good" and "Bad", respectively.

  4. By the end of task 5, you will be able to provide contrastive explanations for decisions affecting individual cases.

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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。

您可以从指导项目中下载并保留您创建的任何文件。为此,您可以在访问云桌面时使用‘文件浏览器’功能。

指导项目不符合退款条件。请查看我们完整的退款政策

指导项目不提供助学金。

指导项目不支持旁听。

您可在页面顶部点按此指导项目的经验级别,查看任何知识先决条件。对于指导项目的每个级别,您的授课教师会逐步为您提供指导。

是,您可以在浏览器的云桌面中获得完成指导项目所需的一切。

您可以直接在浏览器中于分屏环境下完成任务,以此从做中学。在屏幕的左侧,您将在工作空间中完成任务。在屏幕的右侧,您将看到有授课教师逐步指导您完成项目。