Classification is a type of machine learning problem which aims to predict a class label for a given input data point. It is one of the most widely used techniques in machine learning and is applied in various domains like image classification, spam detection, sentiment analysis and many more.
There are two main types of classification problems: binary classification and multi-class classification. In binary classification, the goal is to predict one of two possible class labels, such as yes or no, true or false, or spam or not spam.
Multiclass classification involves predicting more than two possible class labels.Machine learning algorithms used for classification fall into two broad categories: linear and nonlinear. Linear algorithms, such as logistic regression, linear discriminant analysis and support vector machines, work by drawing a straight line or plane that separates the different classes.
The performance of a classification algorithm is typically measured by metrics such as :
Accuracy measures the percentage of correctly predicted class labels, while precision measures the percentage of true positives among all positive predictions. Recall measures the percentage of true positives among all actual positive instances, and F1 score is the harmonic mean of precision and recall.