Classification

Assigning predefined labels or categories to input data.

Overview

Classification is a supervised learning task in which a model assigns a predefined label or category to each input. Examples range from detecting spam emails to identifying whether an image contains a cat or dog, or determining the sentiment of a piece of text.

Why Classification Matters

  • Decision Structuring
    Classification supports various decision-making processes by organizing data into recognizable groups. Examples include fraud detection, email filtering, or medical diagnostics.

  • Data Organization
    It provides a systematic way to handle large volumes of information. Once labeled, data can be further analyzed or used to inform subsequent steps in an AI pipeline.

How Classification Works

  1. Training Data
    A model is trained on labeled examples—each example has known inputs and expected outputs.
  2. Learning Patterns
    The model identifies underlying patterns in the training data that correlate inputs to labels.
  3. Prediction
    Once trained, the model applies these learned patterns to new, unseen inputs to predict labels.

Types of Classification

  1. Binary Classification
    Involves exactly two labels (e.g., "junk" vs. "not junk" in the case of email filtering).
  2. Multi-Class Classification
    Deals with more than two categories (e.g., classifying images as “cat,” “dog,” or “bird”).
  3. Multi-Label Classification
    An input can be assigned multiple labels at once (e.g., tagging an image with both “cat” and “outdoors”).
  4. Imbalanced Classification
    Occurs when one label is far more common than others. Various techniques (oversampling, undersampling, etc.) aim to adjust for these imbalances.

Common Applications

  • Email Filtering
    Determining whether a message is spam or legitimate.
  • Image Recognition
    Identifying objects in images (cats, dogs, vehicles, and more).
  • Sentiment Analysis
    Classifying text by sentiment (positive, negative, or neutral).
  • Medical Diagnosis
    Helping categorize diagnostic images or test results, subject to accuracy and proper clinical context.