What is CatBoost
CatBoost or Categorical Boosting is a machine learning algorithm developed by Yandex, a Russian multinational IT company. This special boosting algorithm is based on the gradient boosting framework and is designed to handle categorical features more effectively than traditional gradient boosting algorithms. CatBoost incorporates techniques like ordered boosting, oblivious trees, and advanced handling of categorical variables to achieve high performance with minimal hyperparameter tuning. CatBoost classifier is an implementation of the CatBoost algorithm which is specifically used for classification task. CatBoost is very efficient for multiclass classification problems. Next we will see a step-by-step guide for implementation of CatBoost classifier for Multiclass classification using Iris dataset. Then a detailed Exploratory Data Analysis is given for understanding dataset. After that, we will develop a user interface where this model will run in background and users can give input and get real-time predicted results.
Multiclass classification using CatBoost
Multiclass or multinomial classification is a fundamental problem in machine learning where our goal is to classify instances into one of several classes or categories of the target feature. CatBoost is a powerful gradient-boosting algorithm that is well-suited and widely used for multiclass classification problems. In this article, we will discuss the step-by-step implementation of CatBoost for multiclass classification.
Table of Content
- What is Multiclass Classification
- What is CatBoost
- Implementation of Multiclass classification using CatBoost
- Exploratory Data Analysis
- Model Training and Evaluation
- Model Deployment
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