12-01, 10:30–11:00 (UTC), Talk Track II
It’s crunchy! It’s sweet! Maybe it is the presence of the nuts or their absence. There are various features that make you favor a particular cereal. Now surely, if we modeled the consumer ratings for cereals, some features would be considered more important than others. After all, feature engineering is one of the most critical steps in modeling. But after the model is up and running, what if we tweak the features just to see how much meddling can affect the preference? This process is called post-hoc feature attribution and it seeks to interpret the model behavior. In this talk, let us spoon through the interpretability of ML models.
Explainable AI (XAI) and Interpretable Machine Learning (IML) are exciting new fields that are nudging AI development towards more transparent modeling. They bring accountability and transparency to a virtual black box, that is, a trained model working for you. The model could be sitting behind a snazzy user interface or a document, or it could be a trained model that you as a data scientist would now like to enhance.
In the post-modeling aka post-hoc stage, interpretability aims at the understanding of dynamics between the input features and the output predictions, i.e., it would help understand the contribution of the features to the model predictions.
In the proposed talk, I would gently introduce you to the above concepts and some common methods of realizing them. Lastly, not to act like a cereal killer but let’s seek interpretations from a cereal rating dataset!
My talk will focus on
* How are explainability and interpretability different?
* Types of interpretations in IML.
* Python code to seek interpretability from a model trained on the cereals rating dataset.
No previous knowledge expected
Hello there! I am a blogger who writes about AI and privacy. Currently, wrapping up my Ph.D. in data science. I'm a dog mom and love painting watercolors.