What is CatBoost?
CatBoost is a machine learning library that was designed to speed up the training of deep neural networks. It can also be used for text classification, as it can be used to predict whether an article belongs to a given topic or not. This algorithm is based on the use of decision trees and it is designed to make predictions with the help of a training data set.
CatBoost was developed by researchers at the University of Montreal, McGill University and Google Brain. It is a type of gradient boosting algorithm that uses tree-based techniques for training deep neural networks. The technique was first introduced in 2017 in an academic paper and it has been used for research purposes only.
The CatBoost algorithm improves accuracy by adjusting the weights according to the data distribution and by incorporating prior knowledge about the data set. This can help to reduce overfitting and improve generalization performance.
The main goal of CatBoost is to provide a framework for gradient boosting, which is an ensemble machine learning method for supervised learning, that can be used with different loss functions and optimization algorithms.
Gradient boosting is a machine learning technique that can be used to create a prediction model by iteratively training models on datasets, with each successive model building on the previous models’ predictions and errors.
The library provides implementations of gradient boosting trees with various characteristics, such as classification and regression trees, random forest trees, and extreme gradient boosting trees.