Class AdaBoostR2RegressionOptions

Namespace

AiDotNet.Models.Options

Assembly

AiDotNet.dll

Configuration options for the AdaBoost R2 regression algorithm.

public class AdaBoostR2RegressionOptions : DecisionTreeOptions

Inheritance

object

ModelOptions

DecisionTreeOptions

AdaBoostR2RegressionOptions

Inherited Members

DecisionTreeOptions.MaxDepth

DecisionTreeOptions.MinSamplesSplit

DecisionTreeOptions.MaxFeatures

DecisionTreeOptions.SplitCriterion

DecisionTreeOptions.UseSoftTree

DecisionTreeOptions.SoftTreeTemperature

ModelOptions.Seed

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

AdaBoost R2 is an ensemble learning method that combines multiple decision trees to create a more powerful regression model.

For Beginners: AdaBoost (Adaptive Boosting) is like having a team of experts (decision trees) working together to solve a problem. Each expert specializes in fixing the mistakes made by previous experts. The "R2" indicates this is a version designed specifically for regression problems (predicting continuous values like prices or temperatures) rather than classification problems (categorizing data into groups).

This class inherits from DecisionTreeOptions, which means it includes all the configuration options for decision trees plus additional options specific to AdaBoost R2.

Properties

NumberOfEstimators

Gets or sets the number of decision tree estimators (weak learners) to use in the ensemble.

public int NumberOfEstimators { get; set; }

Property Value

int

The number of estimators, defaulting to 50.

Remarks

For Beginners: This setting controls how many "experts" (decision trees) will be in your team. More trees can lead to better predictions but will take longer to train and use more memory. The default value of 50 is a good starting point for most problems.

Increasing this value may improve model accuracy up to a point, but with diminishing returns. Very high values might lead to overfitting (when the model performs well on training data but poorly on new data).