Classification is a type of machine learning task where the goal is to predict the class or category of a new observation based on a set of training data. In a classification problem, the input data consists of a set of features or attributes, and the output is a categorical label or class.
There are two main types of classification problems: binary classification and multiclass classification. In binary classification, the output is a binary variable, meaning it can take on one of two possible values, such as 0 or 1. Examples of binary classification problems include spam detection, fraud detection, and medical diagnosis.
In multiclass classification, the output is a categorical variable that can take on more than two possible values, such as the type of flower (setosa, versicolor, or virginica) based on its petal length and width. Examples of multiclass classification problems include image recognition, sentiment analysis, and speech recognition.
Classification algorithms can be supervised or unsupervised. In supervised classification, the algorithm is trained on a labeled dataset, where each observation is assigned a known class label. Common supervised classification algorithms include logistic regression, decision trees, random forests, and support vector machines.
In unsupervised classification, the algorithm is trained on an unlabeled dataset, where there are no known class labels. The goal of unsupervised classification is to discover natural groupings or clusters in the data. Common unsupervised classification algorithms include k-means clustering, hierarchical clustering, and Gaussian mixture models.
Classification is a fundamental problem in machine learning, and there are many different algorithms and techniques that can be used to solve classification problems, depending on the specific application and the characteristics of the data.