Classification Types
Classification is the task of categorizing data into a relatively small set of classes. There are broadly two different classification types:
Binary Classification
Multiclass Classification
Binary classification is a type of supervised machine learning problem where the goal is to predict one of two possible outcomes for a given input data. The two possible outcomes are typically represented as 0 (negative class) and 1 (positive class), although any binary labeling scheme can be used.
For example, a binary classifier might be used to predict whether a customer will make a purchase or not, whether an email is spam or not, or whether a medical image contains a specific type of abnormality or not.
Multiclass classification is a type of supervised machine learning problem where the goal is to predict one of more than two possible outcomes for a given input data. In contrast to binary classification, which only has two possible outcomes, multiclass classification problems can have any number of possible outcomes.
For example, a multiclass classifier might be used to predict the type of an animal based on its features, the genre of a movie based on its content, or the type of a plant species based on its physical characteristics.
Evaluating the performance of a multiclass classifier is typically done using metrics such as accuracy, precision, recall, F1 score, and AUC-ROC curve. These metrics measure how well the model is able to distinguish between the different classes, and how well it is able to make correct predictions. These metrics are explained in detail in Advanced Statistics article under Dashboard widgets category.
It's important to note that the specific evaluation metrics used for multiclass classification may depend on the problem at hand, and it may also be necessary to modify the metrics in order to account for the fact that the model is making multiple predictions.
Binary Classification
Multiclass Classification
Binary Classification
Binary classification is a type of supervised machine learning problem where the goal is to predict one of two possible outcomes for a given input data. The two possible outcomes are typically represented as 0 (negative class) and 1 (positive class), although any binary labeling scheme can be used.
For example, a binary classifier might be used to predict whether a customer will make a purchase or not, whether an email is spam or not, or whether a medical image contains a specific type of abnormality or not.
Multiclass Classification
Multiclass classification is a type of supervised machine learning problem where the goal is to predict one of more than two possible outcomes for a given input data. In contrast to binary classification, which only has two possible outcomes, multiclass classification problems can have any number of possible outcomes.
For example, a multiclass classifier might be used to predict the type of an animal based on its features, the genre of a movie based on its content, or the type of a plant species based on its physical characteristics.
Evaluating Classification Algorithms
Evaluating the performance of a multiclass classifier is typically done using metrics such as accuracy, precision, recall, F1 score, and AUC-ROC curve. These metrics measure how well the model is able to distinguish between the different classes, and how well it is able to make correct predictions. These metrics are explained in detail in Advanced Statistics article under Dashboard widgets category.
It's important to note that the specific evaluation metrics used for multiclass classification may depend on the problem at hand, and it may also be necessary to modify the metrics in order to account for the fact that the model is making multiple predictions.
Updated on: 30/01/2023
Thank you!