Interpretable machine learning applications: Part 3

提供方
Coursera Project Network
在此免費的指導 項目中,您將:

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.

在面試中展現此實踐經驗

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

必備條件

Some introductory knowledge in machine learning and statistics. Some familiarization with Python programming environments.

您要培養的技能

Training and testing an Artificial Neural NetworkUsing the Protodash algorithmUsing keras as API for TensorFlowNormalization of data prior to training a prediction modelExplanations based on similarity measurements

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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.

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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常見問題

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