Hi Reader,

How's your January going? I've been playing a lot of Pokémon (the card game) with my 7 year-old... actually I'm the one who's always bugging him to play 😂

Are you a Pokémon fan? Meowscarada ex, anyone?

🔗 Link of the week

​Introduction to Polars (Practical Business Python)

Have you heard of Polars? In short, it's a high-performance, memory-efficient alternative to pandas. If you're new to Polars, this blog post walks through basic Polars code and compares it to pandas.

👉 Tip #37: Simplify data preprocessing with this scikit-learn model

When performing supervised Machine Learning, one of the keys to success is effective data preprocessing, which can require a lot of thought and planning.

However, there's a scikit-learn model which has two magical properties that significantly reduce your preprocessing burden. (And you've probably never even heard of it!)

It's called Histogram-Based Gradient Boosted Trees (HGBT). Here are its magical properties:

1. Native support for missing values
2. Native support for categorical features

What exactly does that mean? I'll explain below! 👇

1️⃣ Native support for missing values

When your training data contains missing values, normally you have to impute all missing values as part of the data preprocessing step. (The only alternative is to drop samples or features with missing values, which can mean losing valuable training data!)

But if you use HGBT, it will handle the missing values without imputation or dropping any data!

Here's a minimal example from the scikit-learn documentation:

Here's a longer example from one of my videos.

Note: Decision Trees also support missing values as of scikit-learn 1.3, and Random Forests support missing values as of scikit-learn 1.4.

2️⃣ Native support for categorical features

When your training data contains unordered categorical features, normally you have to one-hot encode them as part of the data preprocessing step.

But if you use HGBT, it will handle the categorical features without encoding!

Starting in scikit-learn 1.4, HGBT will also infer which features are categorical directly from the data types of a pandas DataFrame!

Here's an example from the scikit-learn documentation:

Here's a longer comparison of native categorical support versus one-hot encoding and ordinal encoding.

Going further

If you want to learn more HGBT, check out the scikit-learn user guide.

Or if you're new to scikit-learn, check out one of my FREE scikit-learn courses!

👋 Until next time

Did you like this week’s tip? Please send it to a friend or share this link on social. It really helps me out! 🙌

See you next Tuesday!

- Kevin

P.S. Microsoft Excel World Championship (with live commentary)​

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