Working with Large-Scale One-Hot Encoding: A Memory-Efficient Approach

Tame Your RAM-Hungry Categorical Variables Without Breaking Your Machine

Hey everyone! Recently, I've been diving deep into handling massive datasets, and today I want to share a clever workaround I discovered while tackling the Criteo Advertising Competition on Kaggle. Trust me, this one's going to be good!

The Challenge

Picture this: You've got an 11GB training dataset with categorical variables that can take millions of unique values. Your first instinct? "Let me just load it into pandas and use scikit-learn's DictVectorizer." Well, spoiler alert - your RAM's gonna tap out faster than a rookie in a marathon!

Even with my beefy 16GB machine, I couldn't fit the entire dataset into memory. And while scikit-learn's SGDClassifier has a handy partial_fit method for incremental learning, the same courtesy isn't extended to OneHotEncoder or DictVectorizer. Talk about a pickle! 🥒

Understanding the Data Structure

Before we dive into the solution, let's break down what we're working with:

• 40 features total

• 13 continuous variables (I1-I13)

• 26 categorical variables (C1-C2…