What is a Classifier in Machine Learning?
A classifier is any algorithm that sorts data into labeled classes, or categories of information. A simple practical example are spam filters that scan incoming “raw” emails and classify them as either “spam” or “not-spam.” Classifiers are a concrete implementation of pattern recognition in many forms of machine learning.
Why is this Useful?
Classifiers are where high-end machine theory meets practical application. These algorithms are more than a simple sorting device to organize, or “map” unlabeled data instances into discrete classes. Classifiers have a specific set of dynamic rules, which includes an interpretation procedure to handle vague or unknown values, all tailored to the type of inputs being examined. Most classifiers also employ probability estimates that allow end users to manipulate data classification with utility functions.
In unsupervised learning, classifiers form the backbone of cluster analysis and in supervised or semi-supervised learning, classifiers are how the system characterizes and evaluates unlabeled data.
Linear Classifiers (such as Logistic Regression, Naive Bayes Classifier, Fisher's Linear Discriminant, Perceptron)
- Support Vector Machines
- Decision Trees (including Boosted Trees and Random Forest)
- Neural Networks
- Quadratic classifiers
- Kernel estimation (such as Nearest Neighbor)
- Boosting (meta-algorithm)
Learning vector quantization
