{"id":8052,"date":"2021-01-11T03:55:57","date_gmt":"2021-01-11T03:55:57","guid":{"rendered":"https:\/\/wealthrevelation.com\/data-science\/2021\/01\/11\/how-catboost-algorithm-works-in-machine-learning\/"},"modified":"2021-01-11T03:55:57","modified_gmt":"2021-01-11T03:55:57","slug":"how-catboost-algorithm-works-in-machine-learning","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2021\/01\/11\/how-catboost-algorithm-works-in-machine-learning\/","title":{"rendered":"How CatBoost Algorithm Works In Machine Learning"},"content":{"rendered":"
\n
\"Catboost\"Catboost<\/span><\/div>\n
\n

\u00a0 CatBoost is the first Russian machine learning algorithm<\/strong><\/a> developed to be open source. The algorithm was developed in the year 2017<\/strong> by machine learning researchers and engineers at Yandex<\/strong> (a technology company).<\/p>\n

The intention is to serve multi-functional purposes such as<\/p>\n

CatBoost algorithm is another member of the gradient boosting<\/strong><\/a> technique on decision trees<\/strong><\/a>.<\/p>\n<\/div>\n

\n
\n
\n

Learn the popular CatBoost algorithm in machine learning, along with the implementation. #machinelearning #datascience #catboost #classification #regression #python<\/p>\n<\/p><\/div>\n

\n\t\t\t
\n\t\t\t\t<\/i>
\n\t\t\t\tClick to Tweet<\/span>
\n\t\t\t<\/span>\n\t\t<\/p>\n<\/p><\/div>\n<\/div>\n

\n

One of the many unique features that the CatBoost algorithm offers is the integration to work with diverse data types<\/strong> to solve a wide range of data problems faced by numerous businesses.\u00a0<\/p>\n

Not just that, but CatBoost also offers accuracy<\/strong><\/a> just like the other algorithm in the tree family.<\/p>\n

Before we get started, let\u2019s have a look at the topics you are going to learn in this article.<\/p>\n<\/div>\n

What is CatBoost Algorithm?<\/h2>\n
\"What\"What<\/span><\/div>\n
\n

The term CatBoost is an acronym that stands for “Category\u201d and \u201cBoosting<\/strong><\/a>.\u201d Does this mean the \u201cCategory\u2019 in CatBoost means it only works for categorical features?<\/p>\n

The answer is, \u201cNo.\u201d<\/strong><\/p><\/blockquote>\n

According to the CatBoost documentation, CatBoost supports numerical<\/strong>, categorical<\/strong>, and text features<\/strong> but has a good handling technique for categorical data.\u00a0<\/p>\n

The CatBoost algorithm has quite a number of parameters to tune the features<\/strong><\/a> in the processing stage.<\/p>\n

“Boosting” in CatBoost refers to the gradient boosting machine learning<\/strong><\/a>. Gradient boosting is a machine learning technique for regression and classification<\/strong><\/a> problems.\u00a0<\/p>\n

Which produces a prediction model in an ensemble of weak prediction models, typically decision trees<\/strong><\/a>.\u00a0<\/p>\n

Gradient boosting is a robust machine learning algorithm that performs well when used to provide solutions to different types of business problems such as <\/p>\n

Again, it can return an outstanding result with relatively fewer<\/strong> data. Unlike other machine learning algorithms that only perform well after learning from extensive data.<\/p>\n

We would suggest you read the article How the gradient boosting algorithms works<\/strong><\/a> if you want to learn more about the gradient boosting algorithms functionality.<\/p>\n

Features of CatBoost<\/h2>\n<\/div>\n
\"CatBoost\"CatBoost<\/span><\/div>\n
\n

Here we would look at the various features the CatBoost algorithm offers and why it stands out.<\/p>\n

Robust<\/h3>\n

CatBoost can improve the performance<\/strong> of the model while reducing overfitting<\/strong><\/a> and the time spent on tuning. \u00a0<\/p>\n

CatBoost has several parameters to tune. Still, it reduces the need for extensive hyper-parameter tuning<\/strong><\/a> because the default parameters<\/strong> produce a great result.<\/p>\n

Accuracy<\/h3>\n

The CatBoost algorithm is a high performance and greedy novel<\/strong> gradient boosting implementation.\u00a0<\/p>\n

Hence, CatBoost (when implemented well) either leads or ties in competitions with standard benchmarks.<\/p>\n

Categorical Features Support<\/h3>\n

The key features of CatBoost is one of the significant reasons why it was selected by many boosting algorithms such as LightGBM, \u00a0XGBoost algorithm<\/strong><\/a> ..etc\u00a0<\/p>\n

With other machine learning algorithms. After preprocessing and cleaning your data, the data has to be converted into numerical features<\/strong> so that the machine can understand and make predictions. <\/p>\n

This is same like, for any text related models we convert the text data into to numerical data it is know as word embedding techniques<\/strong><\/a>.<\/p>\n

This process of encoding or conversion is time-consuming. CatBoost supports working with non-numeric factors, and this saves some time plus improves your training results.<\/p>\n

Easy Implementation<\/h3>\n

CatBoost offers easy-to-use interfaces. The CatBoost algorithm can be used in Python with scikit-learn<\/strong><\/a>, R, and command-line interfaces.<\/p>\n

Fast and scalable GPU version: the researchers and machine learning engineers designed CatBoost at Yandex to work on data sets as large as tens of thousands of objects without lagging.\u00a0<\/p>\n

Training your model on GPU<\/strong> gives a better speedup when compared to training the model on CPU.\u00a0<\/p>\n

To crown this improvement, the larger the dataset is, the more significant the speedup. CatBoost efficiently supports multi-card configuration<\/strong>. So, for large datasets, use a multi-card configuration.<\/p>\n

Faster Training & Predictions<\/h3>\n

Before the improvement of servers, the maximum number of GPUs per server is 8 GPUs. Some data sets are more extensive than that, but CatBoost uses distributed GPUs.\u00a0<\/p>\n

This feature enables CatBoost to learn faster and make predictions 13-16 times faster than other algorithms.<\/p>\n

Supporting Community of Users<\/h3>\n

The non-availability of a team to contact when you encounter issues with a product you consume can be very annoying. This is not the case for CatBoost.\u00a0<\/p>\n

CatBoost has a growing community where the developers lookout for feedbacks and contributions. <\/p>\n

There is a Slack community, a Telegram channel (with English and Russian versions), and Stack Overflow support. If you ever discover a bug, there is a page via GitHub for bug reports.<\/p>\n

Is tuning required in CatBoost?<\/h2>\n

The answer is not straightforward<\/strong> because of the type and features of the dataset. The default<\/strong> settings of the parameters in CatBoost would do a good job.\u00a0<\/p>\n

CatBoost produces good results without extensive hyper-parameter tuning<\/strong><\/a>. However, some important parameters can be tuned in CatBoost to get a better result.\u00a0<\/p>\n

These features are easy to tune and are well-explained in the CatBoost documentation. Here are some of the parameters that can be optimized for a better result; <\/p>\n