Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math

[@adataodyssey]

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[@josephbolton8092]

This was so great

[@adataodyssey]

Thanks Joseph :)

[@myselfandpesit]

Clear explanation. Thanks!

[@adataodyssey]

No problem :)

[@makefly3305]

Hi I have a question at 5:45, wanna know based on which pattern of the plot you said the "km_driven" is less equally distributed and skewed to the left? 😄

[@adataodyssey]

I'm looking at the bars on the x-axis. This is known as a "rug plot". 10% of the dataset falls before the first bar, 20% before the second bar and so on... You can see that the bars are shifted towards the left. This means that most of the dataset has a lower km_driven value. I hope that makes sense?

[@IsmaelSilva-po2xb]

Writing a subsection on ICE on my master dissertation

[@adataodyssey]

Great Ismael! I hope this video helped :)

[@TheCsePower]

Would be nice if the pdp had some kind of confidence interval that varied with the feature value.

[@adataodyssey]

That's a good idea! You might be able to use the std of the prediction around each point. It would be related to the ICE plot where a point would have a larger std if not all the individual lines follow the same trend.