👩‍💻 Code: github.com/justmarkham/scikit-learn-tips 🤖 Learn ML from me: courses.dataschool.io/ml-courses 💌 Weekly Data Science tips: tuesday.tips/ Thanks for watching! 🙌

Amazing as always. I have been following you since 2019 and every time it's something new.

These are amazing! I learned a lot!

Great video, very informative. Thank you so much for sharing.

24:08 handle_unknown='ignore'. A most useful tip! If only I'd read the docs. But, I don't understand when you say to go back and include the previously unknown categories. How can you train on unknown data? Even if you include the unknown "labels" in your encoder, they will all be zero during training, because, obviously, they weren't in your training data. I think it's best to just leave it alone. If it wasn't in your training data, then it's probably a rare occurrence and you can just ignore it. Zeros in all known categories simplifies what happens down stream? If you want to train on unknown data, you would need to use "dummy data" and set min_frequency or max_categories, then handle_unknown='infrequent_if_exists' to give down steam modules something to work with.

This is a Masters level info on Data science.

Excellent, well done and thank you!

I'm a new subscriber. I'm so glad I found u amazing explanation

Hello Kevin! Thank you for your great work and tips. Could you please include in the repository notebooks for the tips that are missing? I suppose those are the ones that do not contain code. However, it would be great to have those included in some way so nothing is missing when someone would like to do a quick review. Again, thank you so much for your sharing!

Awsome resource for Machine Learning. Thanks!

2:10:00 Yeah, if you have the time and the determination, you could run DecisionTreeClassifier, then plot_tree, and look through it for conditions like name != value. Then, you could use the order the decision tree "discovers" categories as the ordinal value for that feature, 0 being first. You just need to write a custom transformer to preprocess your validation data and assign -1 to all unknowns. Another trick I've had success with is ordering by frequency, with 0 being the most frequent. In that case, your custom transformer should assign 0 to all unknowns. Easy-peasy.

We need more videos like these.

Super useful...Thanks Kevin

2:09:40 Hopefully, you'll never have 200 columns to passthrough, but I think specifying which columns to passthrough makes what you intend clearer. The default is remainder=drop, so the author thought that as well.

Thank you Kevin.

Thanks Kevin!

Good work❤

thank you 🙏

30:20 Missingness. So, what happens when a feature is fully populated in your training data, but has missing values in your validation data? Just bringing that up in case you don't get to it.

U r awesome sir

2:03:00 Drop=if_binary makes sense, otherwise you have two columns which are perfectly redundant, not just implied. At least, it's a happy compromise. My only hesitation, without playing with it, is that the order is probably alphabetic. If it assigned 0 to the most frequent category, then handle_unknown=ignore would make sense. Otherwise, you're lumping unknowns in with the "least" alphabetic category. That's kinda silly.

Please I want a video of (PSO) with RF in jupyter

Hey kevin, why aren't you bringing some new videos anymore? :(

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