A set of scikit-learn-style transformers for encoding categorical variables into numeric with different techniques.
Category Encoders is a Python library for encoding categorical variables for machine learning tasks. It is available on contrib.scikit-learn.org and extends the capabilities of scikit-learn’s preprocessing module.
The library provides several powerful encoding techniques for dealing with categorical data, including:
Ordinal encoding: maps categorical variables to integer values based on their order of appearance
One-hot encoding: creates a binary feature for each category in a variable
Binary encoding: maps each category to a binary code
Target encoding: encodes each category with the mean target value for that category
Hashing encoding: maps each category to a random index in a hash table
Category Encoders also supports a range of advanced features, such as handling missing values, combining multiple encoders, and applying encoders to specific subsets of features.
Overall, Category Encoders is a useful tool for preprocessing categorical data and improving the accuracy and performance of machine learning models.
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The main idea in the paper is that the performance of regular Multi-layer Perceptron (MLP) can be significantly improved if we use Transformers to transforms regular categorical embeddings into contextual ones.
The TabTransformer is built upon self-attention based Transformers. The Transformer layers transform the embed- dings of categorical features into robust contextual embed- dings to achieve higher prediction accuracy.