What is CatBoost?

CatBoost is a machine learning library developed by Yandex, designed specifically for gradient boosting on decision trees. What sets CatBoost apart is its ability to handle categorical features seamlessly without the need for preprocessing, such as one-hot encoding or label encoding. This feature simplifies the workflow and can save considerable time and effort, particularly in scenarios where datasets contain a mix of numerical and categorical features.

In addition to its support for categorical features, CatBoost offers several other key features that contribute to its effectiveness. It incorporates various optimization techniques for both training and inference speed, making it suitable for large datasets and real-time applications. The library also includes built-in feature importance calculation, enabling users to understand the relative importance of different features in their models.

CatBoost employs the gradient boosting algorithm, which sequentially builds an ensemble of decision trees, with each subsequent tree correcting the errors of the previous ones. To prevent overfitting, CatBoost incorporates regularization techniques such as L2 regularization and gradient-based random feature selection.

Furthermore, CatBoost supports GPU training, allowing for faster training times, particularly beneficial for large datasets. This combination of features makes CatBoost a powerful tool in the realm of gradient boosting, particularly well-suited for datasets with categorical features and applications requiring high performance and efficiency.

Key Features:

  1. Categorical Feature Support - CatBoost can handle categorical features naturally, without requiring preprocessing like one-hot encoding or label encoding. This makes it convenient for datasets with a mix of numerical and categorical features.

  2. Built-in Feature Importance - It provides built-in feature importance calculation, helping users understand the importance of different features in the model.

  3. Optimized for Speed - CatBoost is optimized for both training speed and inference speed, making it suitable for large datasets and real-time applications.

  4. Gradient Boosting - It employs the gradient boosting algorithm, which sequentially builds an ensemble of decision trees, each one correcting the errors of the previous ones.

  5. Regularization Techniques - CatBoost incorporates various regularization techniques to prevent overfitting, such as L2 regularization and gradient-based random feature selection.

  6. Supports GPU Training - It offers GPU training support, enabling faster training times, especially for large datasets.

Sweet cats like CatBoost love to play, especially on decision trees.

CatBoost explained:

CatBoost works by combining many decision trees to make predictions. Each decision tree learns from the data to predict the outcome, and CatBoost combines the predictions of all these trees to make a final prediction.

Here's a simple breakdown of how it works:

  1. Decision Trees - CatBoost uses decision trees as its basic building blocks. Think of a decision tree as a series of yes/no questions that help to categorize or predict an outcome. For example, if you were trying to predict whether someone would like a certain type of movie, the decision tree might ask questions like "Is the movie animated?" or "Is the movie longer than 2 hours?"

  2. Ensemble Learning - CatBoost doesn't rely on just one decision tree; instead, it creates many decision trees, each one learning from different parts of the data. This ensemble of trees works together to make more accurate predictions than any single tree could make on its own.

  3. Handling Categorical Data - One of CatBoost's strengths is its ability to handle categorical data without needing preprocessing. Categorical data refers to data that represents categories, like different types of movies or colors of cars. CatBoost is designed to work directly with this type of data, which can make it more efficient and easier to use for certain types of problems.

  4. Gradient Boosting - CatBoost uses a technique called gradient boosting to build its ensemble of decision trees. In simple terms, gradient boosting works by repeatedly training new trees to correct the mistakes of the previous ones. Each new tree focuses on the data points that the previous trees struggled with, gradually improving the overall prediction accuracy.

  5. Combining Predictions - Once all the trees are trained, CatBoost combines their individual predictions to make a final prediction. This combination process takes into account the strengths and weaknesses of each tree, resulting in a more accurate and robust prediction.

Overall, CatBoost is a powerful machine learning algorithm that excels at handling categorical data and making accurate predictions by combining the strengths of multiple decision trees.

History of CatBoost:

CatBoost, developed by Yandex, was first introduced in 2017. It emerged as a response to the challenges posed by categorical features in machine learning tasks, particularly in tabular data. Traditional gradient boosting implementations struggled with categorical variables, often requiring preprocessing steps like one-hot encoding or label encoding, which could be cumbersome and time-consuming, and might lead to dimensionality issues.

CatBoost aimed to address these challenges by providing a solution that could handle categorical features directly, without the need for preprocessing. The "Cat" in CatBoost stands for "Categorical Boosting" (and for the sweet, furry animal much to my regret), highlighting its focus on efficiently handling categorical variables.

Since its introduction, CatBoost has gained popularity among data scientists and machine learning practitioners for its effectiveness in handling categorical features and its overall performance in gradient boosting tasks. It has been widely used in various domains, including finance, marketing, healthcare, and more. The development of CatBoost has continued, with updates and improvements aimed at further enhancing its capabilities and performance.

Using CatBoost:

Yandex created it's own package installation tutorial for CatBoost, as well as those explaining how to use it.

But we can offer you simple example of how you can use CatBoost for a binary classification task in Python:

# Importing necessary libraries
from catboost import CatBoostClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Assuming you have already loaded and preprocessed your data
# X_train, X_test: features, y_train, y_test: target labels

# Splitting data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

# Initializing CatBoost classifier with default parameters
catboost_model = CatBoostClassifier()

# Training the model
catboost_model.fit(X_train, y_train)

# Making predictions on the testing set
y_pred = catboost_model.predict(X_test)

# Evaluating model performance
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

This is a basic example to get you started. You can customize the model by tuning hyperparameters, handling categorical features differently, or performing feature engineering based on your specific dataset and problem. Also, remember to handle missing values and categorical features appropriately according to your dataset.


"Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili - This book covers various machine learning algorithms and libraries in Python, including popular gradient boosting libraries like XGBoost and LightGBM. Although it may not specifically mention CatBoost, the concepts discussed can be applied to CatBoost as well.

"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron - This book covers a wide range of machine learning topics and tools. While it focuses on Scikit-Learn, it provides a solid foundation in machine learning concepts that can be applied to CatBoost as well.


CatBoost's efficient handling of categorical features and streamlined workflow have simplified machine learning tasks. Its strong performance, speed, and accuracy have made it widely adopted across industries, impacting various applications such as finance, marketing, and healthcare. With its open-source nature and vibrant community, CatBoost continues to advance the field of machine learning, providing valuable solutions for predictive modeling challenges.