Interpretable Machine Learning Applications: Part 2

提供方
Coursera Project Network
在此指导项目中,您将:

Apply Local Interpretable Model-agnostic Explanations (LIME) as a machine learning interpretation

Explain individual predictions being made by a trained machine learning model.

Add aspects for individual predictions in your Machine Learning applications.

Clock90-120 minutes
Beginner初级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

By the end of this project, you will be able to develop intepretable machine learning applications explaining individual predictions rather than explaining the behavior of the prediction model as a whole. This will be done via the well known Local Interpretable Model-agnostic Explanations (LIME) as a machine learning interpretation and explanation model. In particular, in this project, you will learn how to go beyond the development and use of machine learning (ML) models, such as regression classifiers, in that we add on explainability and interpretation aspects for individual predictions. In this sense, the project will boost your career as a ML developer and modeler in that you will be able to explain and justify the behaviour of your ML model. The project will also benefit your career as a decision-maker in an executive position interested in deploying trusted and accountable ML applications. This guided project is primarily targeting data scientists and machine learning modelers, who wish to enhance their machine learning application development with explanation components for predictions being made. The guided project is also targeting executive planners within business companies and public organizations interested in using machine learning applications for automating, or informing, human decision making, not as a ‘black box’, but also gaining some insight into the behavior of a machine learning classifier. Note: This guided project based course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

您要培养的技能

  • Machine Learning Regression Classifiers
  • Programming in Python
  • Performance analysis of prediction models
  • Interpretable and Explainable Models

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. Explore and understand the features and values from the available data about red wine quality

  2. Transform the available data into a classification dataset and problem

  3. Prepare the data for training and validation purposes

  4. Train, validate, estimate, and contrast the performance of three regression classifiers: Decision Tree, Random Forest, AdaBoost

  5. Prepare and train the “explainer” in terms of the LIME library

  6. Display and interpret explanations of individual predictions made by the three classifiers

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

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