What is Multiclass Classification
Multiclass classification is a kind of supervised learning task in machine learning that involves classifying instances into one of three or more distinct classes or categories of the target feature. For multiclass classification, the minimum number of classes of the target feature should be three. In Binary classification tasks, our goal is to classify instances into one of two classes (like fraud detected or no fraud). Still, multiclass classification is used for problems where multiple possible outcomes are there. Multiclass classification can be used in various fields of Natural Language Processing tasks like Sentiment analysis(positive, negative, or neutral), text categorization language identification, etc. Also in speech recognition and recommendation systems(like recommending products, movies, or any content to users based on their preferences and behavior) multiclass classification is used.
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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