Explaining Your Machine Learning Models with SHAP and LIME!

Explaining Your Machine Learning Models with SHAP and LIME!

Explaining Your Machine Learning Models with SHAP and LIME! Helping you to demystify what some people might perceive as a “black box” for your machine learning models.

Helping you to demystify what some people might perceive as a “black box” for your machine learning models

Hello there all! Welcome back again to another data science quick tip. This particular post is most interesting for me not only because this is the most complex subject we’ve tackled to date, but it’s also one that I just spent the last few hours learning myself. And of course, what better way to learn than to figure out how to teach it to the masses?

Before getting into it, I’ve uploaded all the work shown in this post to a singular Jupyter notebook. You can find it at my personal GitHub if you’d like to follow along more closely.

So even though this is a very complex topic behind the scenes, I’m going to intentionally dial it down as much as possible for the widest possible audience. Even though this is ultimately a post designed for data science practitioners, I think it’s equally important for any business person to understand why they should care about this topic.

Prior to jumping into how to calculate/visualize these values, let’s build some intuition on why would we would even care about this topic.

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