Tutorial 1C: Machine Learning Interpretability Tutorial

In this tutorial, we will be working with a default of credit card clients dataset in order to understand and be able to interpret the results from Driverless AI. You will also explore how to launch an experiment, create ML Interpretability report, explore explainability concepts such as Global Shapley, partial dependence plot, decision tree surrogate, K-LIME, Local Shapley, LOCO and individual conditional expectations. 

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Objective
Select the "Read" button to begin.
Select the "Read" button to begin. Explore the goal of this tutorial
Prerequisites
Select the "Read" button to begin.
Select the "Read" button to begin. Find general information about what you need in order to complete this tutorial
Task 1: Launch Experiment and MLI
Select the "Read" button to begin.
Select the "Read" button to begin. Download the dataset and upload it to your lab environment. View the details of your dataset, choose your target column, adjust the experiment settings and launch the experiment.
Task 2: ML Explainability Concepts
Select the "Read" button to begin.
Select the "Read" button to begin. Review general concepts about Machine Learning Explainability such as Response Function Complexity, Scope, Application Domain and others.
Task 3: Global Shapley Values and Feature Importance
Select the "Read" button to begin.
Select the "Read" button to begin. Review important concepts about Global Shapley Values, and Feature Importance; also, check out their respectively plots.
Task 4: Partial Dependence Plot
Select the "Read" button to begin.
Select the "Read" button to begin. Review the concept of Partial Dependence, as well as the Partial Dependence Plot
Task 5: Decision Tree Surrogate
Select the "Read" button to begin.
Select the "Read" button to begin. Explore the concept about Decision Tree Surrogate, as well as the Decision Tree Surrogate Model used in Driverless AI
Task 6: K-LIME
Select the "Read" button to begin.
Select the "Read" button to begin. Review the K-Lime concepts, as well as the K-Lime plot and its Advance Features
Task 7: Local Shapley and LOCO
Select the "Read" button to begin.
Select the "Read" button to begin. Explore Local Shapley and LOCO, and see how they can be used in Driverless AI
Task 8: Individual Conditional Expectation
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Select the "Read" button to begin. View Concepts about ICE Technique
Task 9: Putting It All Together: Dashboard View
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Select the "Read" button to begin. Take a look at the Dashboard View and come up with your own conclusions.
Next Steps
Select the "Read" button to begin.
Select the "Read" button to begin. Check out the next tutorial about Time Series - Retails Sales Forecasting
Quiz
25 Questions  |  2 attempts  |  20/25 points to pass
25 Questions  |  2 attempts  |  20/25 points to pass
ML Interpretability Badge
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  • SC

    After 2 Failed QUIZ attempt , how can take next attempt after reading all the topics. ? Is there any way to re take the quiz after 2 attempt 

    Reply