Class EnsembleFitDetectorOptions

Namespace

AiDotNet.Models.Options

Assembly

AiDotNet.dll

Configuration options for the Ensemble Fit Detector, which combines multiple model fitness detectors to provide more robust and accurate recommendations for algorithm selection.

public class EnsembleFitDetectorOptions

Inheritance

object

EnsembleFitDetectorOptions

Inherited Members

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

An ensemble fit detector evaluates how well different algorithms might perform on a given dataset by combining the opinions of multiple specialized detectors. Each detector in the ensemble focuses on different aspects of the data and problem characteristics.

For Beginners: Think of this as getting advice from a panel of experts instead of just one person. Each expert (detector) specializes in recognizing different patterns in your data. By combining their opinions, you get more reliable recommendations about which AI algorithms will work best for your specific problem. It's like asking several doctors for a diagnosis instead of relying on just one opinion.

Properties

DetectorWeights

Gets or sets the weights applied to each detector in the ensemble.

public List<double> DetectorWeights { get; set; }

Property Value

List<double>

A list of weights for each detector, defaulting to an empty list which will use equal weights.

Remarks

These weights determine how much influence each individual detector has on the final recommendations. Higher weights give more importance to certain detectors. If left empty, all detectors will be weighted equally. The weights are automatically normalized so they sum to 1.0.

For Beginners: This lets you control how much you trust each "expert" in your panel. For example, if you set weights to [2.0, 1.0, 1.0], the first expert's opinion counts twice as much as each of the others. If you leave this empty (the default), all experts' opinions are treated equally. You might want to adjust these weights if you know certain detectors work better for your type of data.

MaxRecommendations

Gets or sets the maximum number of algorithm recommendations to return.

public int MaxRecommendations { get; set; }

Property Value

int

The maximum number of recommendations, defaulting to 5.

Remarks

This limits how many algorithm recommendations the ensemble detector will provide, sorted by their predicted fitness score. This helps focus on the most promising algorithms rather than overwhelming the user with too many options.

For Beginners: This controls how many suggestions you'll get from the system. With the default value of 5, you'll receive the top five AI algorithms that are predicted to work best for your data, ranked from most to least promising. This saves you time by focusing on the most likely successful approaches rather than showing you every possible option.