Machine Learning Explainability

29 augustus, 2024Virtual

1 day
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.

Book this training

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Looking to upskill your team(s) or organization?

Rozaliia will gladly help you further with custom training solutions.

Rozaliia Khafizova
Data and AI Training Advisor

+31 6 11 58 19 37

Get in touch


1 day


09:00 – 17:00









What will you learn?

After the training, you will be able to:

Explain the use cases for model explainability

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

Categorize the used methods into sensitivity vs. impact as well as explaining single predictions (local explainability) vs. multiple predictions (global explainability)

Apply the explainability methods with the provided Python packages

Summarize the advantages and disadvantages for each method

Evaluate whether a method is appropriate for the business use case


  • 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

This training is for you if:

You are proficient with Python’s scikit-learn: you know how to use pipelines, column transformers, linear models, and more complex models (e.g., random forests and gradient boosting).

You want to better understand how your Machine Learning model makes its predictions.

This training is not for you if:

You have never programmed in Python before

You are unfamiliar with core Machine Learning concepts, such as train-test splits, model training and measuring model performance with metrics

You are only interested in achieving the best performance with your models and not how they achieved it

What else should I know?

After registering for this training, you will receive a confirmation email with practical information. A week before the training, the trainer will get in touch to ask you about any requirements you may have and any pre-course tasks you will need to do.

See you soon!


Course Information

All literature and course materials are included in the price

Information on the software and tooling will be shared before the start date

This course requires a laptop

Online courses are delivered via Zoom or Microsoft Teams

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