Table of Contents

Enum PredictionType

Namespace
AiDotNet.Enums
Assembly
AiDotNet.dll

Specifies the type of prediction task that a machine learning model performs.

public enum PredictionType

Fields

BinaryClassification = 0

Represents a binary classification task where the output is one of two possible classes.

For Beginners: Use this when your prediction has only two possible outcomes, like:

  • Yes or No
  • True or False
  • Spam or Not Spam
  • Positive or Negative

Binary predictions typically output a probability between 0 and 1, where:

  • Values closer to 0 indicate the first class (e.g., "No")
  • Values closer to 1 indicate the second class (e.g., "Yes")

Examples: Email spam detection, disease diagnosis, fraud detection

MultiClass = 2

Represents a multi-class classification task where the output is one of many possible classes.

For Beginners: Use this when your prediction can be one of several categories, like:

  • Classifying an image as Cat, Dog, or Bird
  • Categorizing a news article as Sports, Politics, or Technology
  • Predicting a product type from a list of many product categories

Multi-class predictions usually output either:

  • A single class label (e.g., 0, 1, 2, ...)
  • A set of probabilities, one per class (often handled via a separate API)
MultiLabel = 3

Represents a multi-label classification task where multiple labels can be true at the same time.

For Beginners: Use this when each sample can have multiple categories, like:

  • An image that contains both a Dog AND a Person
  • A document tagged with multiple topics (Finance, Legal, HR)

Multi-label predictions are commonly represented as a vector of independent probabilities (one per label), which are then thresholded (e.g., > 0.5) to decide which labels are present.

Regression = 1

Represents a regression task where the output is a continuous numerical value.

For Beginners: Use this when your prediction is a number that can take any value within a range, like:

  • Price of a house
  • Temperature tomorrow
  • Number of sales next month
  • Age of a person from their photo

Unlike Binary prediction, Regression doesn't have fixed categories - it predicts actual numerical values that can be any number (like 42.5, 1000, or -3.14).

Examples: Price prediction, weather forecasting, age estimation, stock market prediction

Remarks

For Beginners: This enum helps you tell the library what kind of prediction you're trying to make. Think of it as telling the AI system what type of question you're asking:

  • Are you asking a yes/no question? Use BinaryClassification.
  • Are you asking "how much" or "what value"? Use Regression.

Choosing the right prediction type helps the AI model understand what you're trying to accomplish and use the appropriate techniques for your specific problem.

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