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? 😄
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?
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.
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This was so great
Thanks Joseph :)
Clear explanation. Thanks!
No problem :)
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? 😄
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?
Writing a subsection on ICE on my master dissertation
Great Ismael! I hope this video helped :)
Would be nice if the pdp had some kind of confidence interval that varied with the feature value.
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.