Text Processing in CatBoost

Text features in CatBoost are used to build new numeric features. These features are essential for tasks involving natural language processing (NLP), where raw text data needs to be converted into a format that machine learning models can understand and process effectively.

There are many processes involved in CatBoost’s text processing:

• Tokenization: The process of dividing text into relevant tokens.
• Embedding: Changing tokens into vectors of numbers.
• Aggregation: Creating fixed-length numerical characteristics by summing these vectors.

Handling Text Features in CatBoost

When dealing with text features, it is crucial to ensure that the order of columns in the training and test datasets matches. This can be managed by using the Pool method in CatBoost, where columns can be added by name.

Example of Using Text Features:

model.fit(x_train, y_train, text_features=['text'])

For prediction, ensure the text features are correctly specified:

preds_class = model.predict(X_test)

Handling text and category data is essential to machine learning to create correct prediction models. Yandex’s gradient boosting library, CatBoost, performs very well. It provides sophisticated methods to convert text characteristics into numerical ones and supports categorical features natively, both of which may greatly enhance model performance. This article will focus on how to transform text features into numerical features using CatBoost, enhancing the model’s predictive power.