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Classification and Regression

Classification

Definition:

Classification is the supervised learning task of predicting a categorical class label from input data. Each example in the dataset belongs to one of a predefined set of classes.

Characteristics:

  • Outputs are discrete.
  • The goal is to assign each input to a single class.
  • Classes can be binary (two classes) or multiclass (more than two classes).

Examples:

  • Classifying emails as spam or not spam (binary classification).
  • Classifying iris flowers into one of three species (multiclass classification),,.

Types of Classification:

  • Binary Classification: Distinguishing between exactly two classes.
  • Multiclass Classification: Distinguishing among more than two classes.
  • Multilabel Classification: Assigning multiple class labels to each instance (less commonly covered in this book).

Key Concepts:

  • The class labels are discrete and come from a finite set.
  • Often expressed as a yes/no question in binary classification (e.g., “Is this email spam?”).
  • The predicted class labels are often encoded numerically but represent categories (e.g., 0, 1, 2 for iris species).

Regression

Definition:

Regression is the supervised learning task of predicting a continuous numerical value based on input features.

Characteristics:

  • Outputs are continuous and often real-valued numbers.
  • The model predicts a numeric quantity rather than a class.

Examples:

  • Predicting a person’s annual income from age, education, and location.
  • Predicting crop yield given weather and other factors.

Key Concepts:

  • Unlike classification, the output is a continuous value.
  • The task is about estimating the underlying function that maps inputs to continuous outputs.
  • Outputs can theoretically be any number within a range, reflecting real-world quantities.

Distinguishing Between Classification and Regression

An intuitive way to differentiate is based on the continuity of the output:

  • If the output is discrete (categorical classes), the problem is classification.
  • If the output is continuous (numerical values), the problem is regression.

Practical Examples and Representations:

  • The Iris dataset is a classic example for classification, with three species as classes.
  • For regression, datasets might involve predicting house prices, temperatures, or yields, with outputs as continuous numbers.
  • Input data can be numerical or categorical, but models require proper encoding and representation (e.g., one-hot encoding for categorical variables).

Summary and Usage

  • Classification and regression are foundational supervised learning tasks.
  • Choosing the right algorithm depends on the nature of the output (categorical vs continuous).
  • Preprocessing and feature representation are critical for both tasks to achieve good performance.
  • Many algorithms can be adapted for either task, but the interpretation and training differ accordingly.

 

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