Classifier
The model or algorithm that performs classification tasks.
Overview
A classifier is the component or algorithm within a classification system that learns from labeled data and then assigns labels to new inputs. Classifiers can be built using various methods, including decision trees, neural networks, or support vector machines.
How Classifiers Function
- Input Processing
The classifier receives input data in a specific format, such as numerical features or transformed text. - Feature Extraction
Depending on the algorithm, some classifiers require explicit feature engineering (e.g., extracting shape descriptors from images), while others learn features automatically (e.g., deep neural networks). - Label Assignment
The classifier applies the patterns it learned during training to assign a label or set of labels to the input.
Types of Classifiers
- Linear Classifiers
Operate by drawing boundaries (or hyperplanes) in the data space (e.g., logistic regression). - Tree-Based Classifiers
Use a sequence of binary decisions (e.g., decision trees, random forests) to separate data. - Kernel-Based Classifiers
Transform data into higher-dimensional spaces (e.g., support vector machines). - Neural Network Classifiers
Use interconnected layers of nodes to learn complex patterns (e.g., multilayer perceptrons, convolutional neural networks).
When to Use a Classifier
- Predicting Categorical Outcomes
Classifiers are chosen whenever the output is a category or label (e.g., “approved” vs. “rejected” for loan applications). - Recognizing Patterns
Identifying consistent traits in data, such as object presence in images. - Decision-Making Tools
Offering structured outputs that can be integrated into broader workflows or decision processes.