Table of Contents

Interface IAsyncTreeBasedModel<T>

Namespace
AiDotNet.Interfaces
Assembly
AiDotNet.dll

Defines an interface for asynchronous tree-based machine learning models.

public interface IAsyncTreeBasedModel<T> : ITreeBasedRegression<T>, INonLinearRegression<T>, IRegression<T>, IFullModel<T, Matrix<T>, Vector<T>>, IModel<Matrix<T>, Vector<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Matrix<T>, Vector<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Matrix<T>, Vector<T>>>, IGradientComputable<T, Matrix<T>, Vector<T>>, IJitCompilable<T>

Type Parameters

T

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

Inherited Members
Extension Methods

Remarks

For Beginners: This interface extends the regular tree-based model interface to add asynchronous (async) capabilities.

Tree-based models are machine learning algorithms that make decisions using a tree-like structure of questions - similar to a flowchart. Popular examples include Decision Trees, Random Forests, and Gradient Boosting Trees.

"Asynchronous" (or "async") means the model can run in the background without blocking other operations. This is especially useful for:

  • Training large models that take a long time
  • Working with web applications where you don't want to freeze the user interface
  • Processing large datasets efficiently

Think of it like ordering food at a restaurant - instead of standing at the counter waiting for your order (synchronous), you get a buzzer and can do other things until your food is ready (asynchronous).

This interface inherits all the regular methods from ITreeBasedModel but adds async versions of the training and prediction methods.

Methods

PredictAsync(Matrix<T>)

Makes predictions asynchronously using the trained model for the given input data.

Task<Vector<T>> PredictAsync(Matrix<T> input)

Parameters

input Matrix<T>

The matrix of input features for which to make predictions.

Returns

Task<Vector<T>>

A Task containing a vector of predicted values, one for each row in the input matrix.

Remarks

For Beginners: After your model is trained, this method uses it to make predictions on new data asynchronously (in the background).

The input parameter is a matrix (table) of features, similar to what you used during training, but these are new examples that the model hasn't seen before.

For example, continuing with the house price prediction:

  • input would contain features of houses you want to predict prices for
  • The returned vector would contain the predicted prices for those houses

The "Async" suffix and Task return type mean this method can run in the background. When the Task completes, you'll get back a Vector containing all the predictions.

TrainAsync(Matrix<T>, Vector<T>)

Trains the tree-based model asynchronously using the provided input features and target values.

Task TrainAsync(Matrix<T> x, Vector<T> y)

Parameters

x Matrix<T>

The matrix of input features where each row represents a sample and each column represents a feature.

y Vector<T>

The vector of target values corresponding to each sample in the input matrix.

Returns

Task

A Task representing the asynchronous training operation.

Remarks

For Beginners: This method teaches the model to make predictions based on your data, but does it asynchronously (in the background).

Parameters explained:

  • x: Your input data organized as a matrix (think of it as a table or spreadsheet)
    • Each row is one example or data point
    • Each column is one feature or characteristic
  • y: The correct answers or target values you want the model to learn to predict
    • One target value for each row in your input matrix

For example, if you're predicting house prices:

  • x would contain features like square footage, number of bedrooms, location, etc.
  • y would contain the actual sale prices of those houses

The "Async" suffix and Task return type mean this method can run in the background while your application continues doing other work.

Edit this page