Machine Learning Explainability

28 February, 2024Virtual

1 day
Virtual
Machine Learning

This one-day training course will help prevent your models from being a black box. It will provide you with a toolbox of Machine-Learning explainability techniques that you can use to explain your models to technical and non-technical people alike. You will also learn when, how, and why you should use the different techniques, and their drawbacks.

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Duration

1 day

Time

09:00 – 17:00

Language

English

Lunch

Included

Certification

No

Level

Professional

What will you learn?

After the training, you will be able to: 

Understand the use cases for model explainability (debug, bias detection, right to explanation, etc.)

Understand when model explainability is not enough (correlation vs. causality, fairness) 

Categorize the methods into sensitivity vs. impact and explain single predictions (local explainability) vs. multiple predictions (global explainability) 

Apply the methods with the provided packages 

Explain the inner workings of all methods Articulate the downsides for each method 

Evaluate whether a method is appropriate for the business use case 

Key takeaways

  1. Break Down Plots for Additive Attributions 
  2. Partial Dependence Plots 
  3. Individual Conditional Expectation Curves / Ceteris Paribus Plots 
  4. Permutation Feature importance 
  5. Shapley and SHAP Values 

Program

This one-day training covers the abovementioned techniques and teaches you how to implement them using Scikit-Learn, Dalex, and Shap. 

  • Introduction to Machine Learning interpretability/explainability 
  • Example-based explanations 
  • The inherent interpretability of ML models 
  • Ceteris Paribus plots 
  • Break-down Plots for Additive Attributions 
  • (Model-specific & Permutation) Feature importance 
  • Partial dependence plots 
  • Shapley and SHAP values 

Who is it for?

This training is perfect for anyone already working with Machine Learning models and wishing to improve and understand them in order to make better predictions.  

Requirements

Proficiency in scikit-learn will be required.

To make the most of this advanced training, knowledge of pipelines, column transformers, linear models, and more complex models (e.g., random forests and gradient boosting), and the core concepts of Machine Learning will be necessary. 

Machine learning models are being entrusted to perform data-driven decision-making. However, these models can often be a “black box”, meaning the motivations behind our decision-making can be incomprehensible. In this training, you will learn techniques to interpret your model and explain its decision to technical and non-technical people alike.  

What else
should I know?

After registering for this course, you will receive a confirmation email with practical information.

See you soon!

Requirements

Travel and accommodation expenses are not included

All literature and course materials are included in the price.

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