CatBoost: gradient boosting with categorical features support (1810.11363v1)

Abstract: In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implementations of gradient boosting in terms of quality on a set of popular publicly available datasets. The library has a GPU implementation of learning algorithm and a CPU implementation of scoring algorithm, which are significantly faster than other gradient boosting libraries on ensembles of similar sizes.

• The paper presents CatBoost, which integrates categorical features directly into the gradient boosting process to reduce preprocessing overhead and overfitting.
• It employs permutation-driven statistical substitution and unbiased gradient estimation methods to effectively mitigate gradient bias.
• The library achieves significant speedups on GPUs and outperforms XGBoost, LightGBM, and H2O in classification accuracy across diverse datasets.

CatBoost: Gradient Boosting with Categorical Features Support

In the paper titled CatBoost: Gradient Boosting with Categorical Features Support, the authors Anna Veronika Dorogush, Vasily Ershov, and Andrey Gulin present a comprehensive overview of CatBoost, a new open-source gradient boosting library specifically designed to handle categorical features efficiently. CatBoost demonstrates superior performance in comparison to existing state-of-the-art gradient boosting implementations like XGBoost, LightGBM, and H2O across diverse datasets.

Introduction to Gradient Boosting and the Need for Categorical Feature Handling

Gradient boosting is widely recognized in the machine learning community for its efficacy in handling heterogeneous features, noisy data, and complex dependencies, making it a go-to technique for tasks such as web search, recommendation systems, and weather forecasting. Traditional implementations of gradient boosting predominantly rely on decision trees as base models and often necessitate the preprocessing of categorical features into numerical ones, which can introduce inefficiencies and overfitting.

CatBoost addresses this limitation by incorporating mechanisms to directly handle categorical features during the training process rather than merely at preprocessing. This approach not only improves the accuracy of the models but also significantly reduces overfitting issues associated with traditional methods.

Handling of Categorical Features

The paper details multiple methods for incorporating categorical features within CatBoost:

1. One-Hot Encoding: This method is advantageous for low-cardinality categorical features and is carried out during the training phase to enhance efficiency.
2. Statistical Substitution: For higher-cardinality features, CatBoost uses aggregated statistics from the labels to substitute categorical values. This process includes an innovative strategy wherein a dataset is permutated, and mean values are calculated incrementally to mitigate overfitting while utilizing the entire dataset for training.

Furthermore, CatBoost allows for the combination of categorical and numerical features, enabling richer interactions and more accurate predictions.

Fighting Gradient Bias

A novel aspect of CatBoost is its mitigation of gradient bias, which is a common problem in standard gradient boosting implementations that results in overfitting. CatBoost employs a process wherein separate models are maintained for unbiased gradient estimation, allowing for more robust tree structures and improved generalization.

Computational Efficiency: GPU and CPU Implementations

CatBoost offers both CPU and GPU implementations, optimizing training and scoring times significantly. The GPU implementation leverages a histogram-based approach for split searching, avoiding the typical slowdowns caused by atomic operations used in other implementations. The GPU version outperforms the CPU version remarkably, with speedup ratios reaching up to 15 times on NVIDIA V100 cards.

The algorithm's integration with GPU is described in detail:

• Histogram-Based Approach for Dense Features: By grouping features and performing efficient memory handling (e.g., perfect hashing for categorical features), the training process is optimized for memory and computational overhead.
• Multiple GPU Support: Feature parallelism is used to exploit multiple GPUs effectively.

Experimental Evaluation

Extensive experimental results provided in the paper underscore CatBoost's performance superiority both in terms of classification accuracy and computational efficiency. For instance, CatBoost shows a consistent reduction in logloss compared to XGBoost, LightGBM, and H2O across various datasets such as Adult, Amazon, and Click.

Scoring Performance

Beyond training, CatBoost also excels in scoring, achieving speedups of up to 60 times compared to LightGBM and 25 times compared to XGBoost when predicting on large-scale datasets.

Practical Implications and Future Work

The capabilities of CatBoost have substantial practical implications, particularly in domains requiring efficient handling of categorical data such as e-commerce, finance, and personalized recommendations. Theoretically, the algorithm's approach to categorical features and gradient bias sets new standards for developing robust machine learning models.

Future research and development could focus on enhancing CatBoost's scalability to even larger datasets and investigating the integration of CatBoost with other automated machine learning frameworks to further streamline the machine learning pipeline. Additionally, exploring hybrid models that combine the strengths of CatBoost with emerging neural network techniques could yield promising avenues for advancements in AI.

In conclusion, the paper on CatBoost presents a highly detailed and robust gradient boosting framework that specifically addresses the challenges associated with categorical features, offering both theoretical insights and practical tools for the machine-learning community.