Table of Contents

Interface IPredictiveModel<T, TInput, TOutput>

Namespace
AiDotNet.Interfaces
Assembly
AiDotNet.dll

Defines the core functionality of a trained predictive model that can make predictions on new data.

public interface IPredictiveModel<T, TInput, TOutput> : IModelSerializer

Type Parameters

T

The numeric data type used for calculations (e.g., float, double).

TInput
TOutput
Inherited Members

Remarks

This interface represents a machine learning model that has been trained and is ready to use.

For Beginners: Think of a predictive model like a calculator that has been specially programmed to solve a specific type of problem. After you've "trained" it with examples (like showing it houses and their prices), it can make educated guesses about new examples (predicting prices for houses it hasn't seen before).

This interface provides the methods you need to:

  • Make predictions with your trained model
  • Get information about how the model was created and how well it performs

Methods

GetModelMetadata()

Retrieves metadata and performance information about the trained model.

ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

A metadata object containing information about the model's performance and configuration.

Remarks

This method provides access to information about how the model was created and how well it performs.

For Beginners: This method gives you a "report card" for your model. It tells you:

  • How accurate the model is
  • What settings were used to create it
  • When it was trained
  • What features (input variables) it uses
  • Other technical details that help you understand its strengths and limitations

This information is useful for:

  • Comparing different models to choose the best one
  • Documenting how your model was created
  • Understanding where your model might make mistakes
  • Deciding if your model needs to be improved or retrained

Predict(TInput)

Makes predictions using the trained model on new input data.

TOutput Predict(TInput input)

Parameters

input TInput

A matrix containing the new data points to make predictions for. Each row is a separate data point, and each column is a feature.

Returns

TOutput

A vector containing the predicted values, one for each input row.

Remarks

This is the primary function of a predictive model - taking new data and generating predictions.

For Beginners: This is where the magic happens! After your model has learned patterns from training data, this method lets you feed it new information and get predictions back.

For example:

  • If you trained a model to predict house prices, you would provide details about new houses and get back predicted prices.
  • If you trained a model to identify spam emails, you would provide new emails and get back predictions about whether they're spam or not.

The input is organized as a matrix where:

  • Each row represents one example (one house, one email, etc.)
  • Each column represents one feature or characteristic (square footage, number of bedrooms, or for emails: number of exclamation marks, certain keywords, etc.)
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