What Classification Meaning, Applications & Example
A supervised learning task that predicts categorical labels.
What is Classification?
Classification is a supervised learning technique in machine learning where a model is trained to assign labels or categories to new data based on patterns learned from labeled training data. It is commonly used in tasks where inputs need to be sorted into predefined classes.
Types of Classification
- Binary Classification: Involves two classes, such as spam vs. not spam in email filtering.
- Multiclass Classification: Involves more than two classes, such as categorizing news articles into topics like sports, politics, or technology.
- Multilabel Classification: Allows each input to belong to multiple classes, like tagging an image with multiple relevant labels (e.g., “beach,” “sunset,” “vacation”).
Applications of Classification
- Email Filtering: Classifies emails as spam or not spam, improving inbox management.
- Medical Diagnosis: Assists in identifying diseases based on patient data, lab results, and imaging.
- Sentiment Analysis : Categorizes text data by sentiment, such as positive, neutral, or negative, often used in social media analysis.
Example of Classification
An example of Classification is in image recognition , where a model classifies images into categories like “cat,” “dog,” or “bird,” enabling applications like object detection in photos.