A classifier in a Machine Learning is a system that inputs a vector of discrete or continuous feature values and outputs a single discrete value, the class.
In machine learning, a classifier is a model that is trained to classify input data into predefined categories or classes based on certain features or attributes. The goal of a classifier is to learn patterns from labeled training data and use that knowledge to predict the class labels of new, unseen instances.
Common types of classifiers include:
- Binary Classifiers: These classifiers classify input data into two distinct classes, such as “spam” or “not spam”, “positive” or “negative”, etc.
- Multiclass Classifiers: These classifiers can classify input data into more than two classes. For example, classifying images into different categories such as “cat”, “dog”, “bird”, etc.
- Probabilistic Classifiers: These classifiers not only predict the class label but also provide the probability or likelihood of each class label. Examples include logistic regression and Naive Bayes classifiers.
- Decision Tree Classifiers: These classifiers use a tree-like structure to make decisions based on the features of the input data.
- Support Vector Machine (SVM): SVM classifiers work by finding the hyperplane that best separates the classes in the feature space.
- Neural Network Classifiers: These classifiers use artificial neural networks to learn complex patterns in the data and make predictions.
The choice of classifier depends on various factors such as the nature of the data, the number of classes, the size of the dataset, computational resources, and the desired performance metrics.