cs-ensembles 1.0.2

Tree Ensemble Algorithms such as TreeBagging and AdaBoosting

Install-Package cs-ensembles -Version 1.0.2
dotnet add package cs-ensembles --version 1.0.2
<PackageReference Include="cs-ensembles" Version="1.0.2" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add cs-ensembles --version 1.0.2
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

cs-ensembles

Ensembles method implemented in C#

Install

Install-Package cs-ensembles

Usage

The sample codes below show how to use the AdaBoosting classifier:

IEnumerable<DDataRecord<string>> training_sample = LoadTrainingSamples();
IEnumerable<DDataRecord<string>> testing_sample = LoadTestingSamples();

AdaBoost<DDataRecord, string> classifier = new AdaBoost<DDataRecord, string>();
classifier.CreateAndTrainWeakClassifiers(training_sample, (t)=>
{
 //create and return a weak classifier such as a decision tree or perceptron
});
classifier.Train(training_sample);

foreach(DDataRecord rec in testing_sample)
{
   string predicted_label = classifier.Predict(rec);
}

The sample codes below show how to use the TreeBagging classifier:

IEnumerable<DDataRecord<string>> training_sample = LoadTrainingSamples();
IEnumerable<DDataRecord<string>> testing_sample = LoadTestingSamples();

TreeBagging<DDataRecord, string> classifier = new TreeBagging<DDataRecord, string>(
(t)=>
{
  //create and return a classifier such as a decision tree or perceptron
}, 900, 2.0 / 3);
classifier.Train(training_sample);

foreach(DDataRecord rec in testing_sample)
{
    string predicted_label = classifier.Predict(rec);
} 

cs-ensembles

Ensembles method implemented in C#

Install

Install-Package cs-ensembles

Usage

The sample codes below show how to use the AdaBoosting classifier:

IEnumerable<DDataRecord<string>> training_sample = LoadTrainingSamples();
IEnumerable<DDataRecord<string>> testing_sample = LoadTestingSamples();

AdaBoost<DDataRecord, string> classifier = new AdaBoost<DDataRecord, string>();
classifier.CreateAndTrainWeakClassifiers(training_sample, (t)=>
{
 //create and return a weak classifier such as a decision tree or perceptron
});
classifier.Train(training_sample);

foreach(DDataRecord rec in testing_sample)
{
   string predicted_label = classifier.Predict(rec);
}

The sample codes below show how to use the TreeBagging classifier:

IEnumerable<DDataRecord<string>> training_sample = LoadTrainingSamples();
IEnumerable<DDataRecord<string>> testing_sample = LoadTestingSamples();

TreeBagging<DDataRecord, string> classifier = new TreeBagging<DDataRecord, string>(
(t)=>
{
  //create and return a classifier such as a decision tree or perceptron
}, 900, 2.0 / 3);
classifier.Train(training_sample);

foreach(DDataRecord rec in testing_sample)
{
    string predicted_label = classifier.Predict(rec);
} 

Release Notes

Tree Ensemble Algorithms such as TreeBagging and AdaBoosting in .NET 4.6.1

Dependencies

This package has no dependencies.

Version History

Version Downloads Last updated
1.0.2 214 5/2/2018
1.0.1 188 5/2/2018