What is Classification in Supervised Learning?
Classification is a fundamental concept in supervised learning, a subset of machine learning wherein models learn from labeled data. In classification tasks, the objective is to predict the categorical label of new data points based on the patterns learned from a training dataset.
How Classification Works
In classification, the algorithm is trained using a dataset containing features (input variables) and their corresponding labels (output variables). The model analyzes the input data to identify underlying patterns and relationships between the features and labels. Once trained, the model can classify unseen instances by assigning them to one of the known categories.
Types of Classification
There are various types of classification problems, including binary classification, where there are two possible classes, and multi-class classification, which involves more than two classes. Examples of classification tasks include email spam detection, sentiment analysis in text, and image recognition.
Common Algorithms
Common algorithms used for classification include logistic regression, decision trees, support vector machines (SVM), and neural networks. Each algorithm has its strengths and is suited for different types of classification tasks depending on the complexity and nature of the data.
In summary, classification is a key technique in supervised learning that enables machines to categorize data effectively, facilitating automated decision-making across diverse applications.