Glossary Library

Technical GlossaryMachine Learning

CatBoost

An advanced ensemble method that combines boosting with strong native handling of categorical variables.

CatBoost is a boosting method that provides important advantages in tabular problems with many categorical variables. Its ability to use categorical features effectively without extensive manual preprocessing makes it attractive in practical data science workflows. It also uses ordered boosting to reduce the risk of leakage. As a result, it stands out as a modern method for both performance and engineering convenience.

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Explore these concepts to continue your artificial intelligence journey.

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Target Encoding

An advanced feature engineering technique that converts categorical levels into numerical representations using target-related summary statistics.

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Linear Regression

A fundamental regression algorithm that models the linear relationship between input variables and a target variable.

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Ridge Regression

A regression method with L2 regularization that reduces overfitting by penalizing coefficient magnitude.