- CatBoost - Home
- CatBoost - Overview
- CatBoost - Architecture
- CatBoost - Installation
- CatBoost - Features
- CatBoost - Decision Trees
- CatBoost - Boosting Process
- CatBoost - Core Parameters
- CatBoost - Data Preprocessing
- CatBoost - Handling Categorical Features
- CatBoost - Handling Missing Values
- CatBoost - Classifier
- CatBoost - Model Training
- CatBoost - Metrics for Model Evaluation
- CatBoost - Classification Metrics
- CatBoost - Over-fitting Detection
- CatBoost vs Other Boosting Algorithms
- CatBoost Useful Resources
- CatBoost - Quick Guide
- CatBoost - Useful Resources
- CatBoost - Discussion
CatBoost - Features
CatBoost is a type of gradient boosting and it can handle both categorical and numerical data. It also works well with minimal effort from the user. It does not need any feature encoding methods, like One-Hot Encoder or Label Encoder to transform category features to numerical ones.
It also uses the symmetric weighted quantile sketch (SWQS) algorithm, which is used to manage missing values in the dataset automatically in order to prevent over-fitting and improve overall dataset performance.
In this chapter we will discuss the key features, as well as examples to help you understand.
Great quality without parameter tuning
CatBoost works effectively out of the box, so you need not spend a lot of time adjusting its settings or hyper-parameters to get remarkable outcomes. This saves a huge amount of time, as selecting the right parameters in machine learning is often complex and time-consuming.
Example: Let's suppose you are building a model to predict values of houses. Many methods need hours or even days of parameter adjustments to improve model performance. So the default settings of CatBoost give excellent results so you do not have to worry of doing any extra work.
Supports categorical features (non-numeric data)
CatBoost can handle non-numerical data with minimal preprocessing like as words or categories. Most machine learning algorithms need numbers to function, so you will have to convert text data to numbers. CatBoost performs this automatically, which saves time and work.
Example: Let us say you are creating a model which is used to predict a success of a product as per its color for example red, blue, or green. Many algorithms will ask you to convert these colors into numbers first. CatBoost handles this for you, making things easier.
Fast and scalable GPU version
CatBoost can work with GPUs (Graphics Processing Units), which somewhat improves it, particularly for large datasets. GPUs outperform traditional CPUs (Central Processing Units) in terms of handling several calculations at once. If you have a large dataset then the speed is even more important.
Example: Let us suppose that you are training a model with millions of rows on a large dataset. If you use a CPU, it can take a few days or even hours. But with the help of CatBoost on a GPU, the same training can be done much faster, in a fraction of the time.
Improved accuracy
CatBoost has a smart learning approach that helps to reduce over-fitting, which occurs when a model becomes overly focused on training data and fails to perform well on new, unknown data. This improves the CatBoost models' accuracy when predicting new data.
Example: Assume you are building a model to predict the popularity of a new song. If your model is too much dependent on training data, it can under perform on new songs. CatBoost's technique helps to avoid this problem, making sure your model performs well with new data.
Fast Prediction
CatBoost learns and predicts faster compared to all other algorithms. It can use several GPUs to learn more quickly, which improves its capacity to predict new outcomes. In some cases, it is 13-16 times faster than traditional machine learning algorithms.
Example: Assume you are building a recommendation system that recommends items to users while they browse an online store. CatBoost allows the system to make fast recommendations in real time, meaning that users get suggestions without having to wait.