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Showing content from http://accord-framework.net/docs/html/T_Accord_MachineLearning_Boosting_Learners_DecisionStump.htm below:

Simple classifier that based on decision margins that are perpendicular to one of the space dimensions.

Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

[SerializableAttribute]
public class DecisionStump : BinaryClassifierBase<double[]>

<SerializableAttribute>
Public Class DecisionStump
	Inherits BinaryClassifierBase(Of Double())

The DecisionStump type exposes the following members.

Computes the output class label for a given input.

Computes class-label decisions for a given set of input vectors.

Computes a class-label decision for a given input.

Computes class-label decisions for the given input.

Computes a class-label decision for a given input.

Determines whether the specified object is equal to the current object.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Serves as the default hash function.

Gets the Type of the current instance.

Teaches the stump classifier to recognize the class labels of the given input samples.

Creates a shallow copy of the current Object.

Views this instance as a multi-class classifier, giving access to more advanced methods, such as the prediction of integer labels.

Views this instance as a multi-label classifier, giving access to more advanced methods, such as the prediction of one-hot vectors.

Returns a string that represents the current object.

Applies the transformation to an input, producing an associated output.

Applies the transformation to a set of input vectors, producing an associated set of output vectors.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Checks whether an object implements a method with the given name.

Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.

The DecisionStump classifier is mostly intended to be used as a weak classifier in the context of an AdaBoostTModel learning algorithm. Please refer to the AdaBoostTModel class for more examples on using the classifier in this scenario. A simple example is shown below:

double[][] inputs =
{
    new double[] {  10, 42 },
    new double[] { 162, 96 },
    new double[] { 125, 20 },
    new double[] {  96,  6 },
    new double[] {   2, 73 },
    new double[] {  52, 51 },
    new double[] {  71, 49 },
};


bool[] outputs =
{
    false, false, true, true, false, false, true
};


var learner = new AdaBoost<DecisionStump>()
{
    Learner = (p) => new ThresholdLearning(),

    
    MaxIterations = 5,
    Tolerance = 1e-3
};


Boost<DecisionStump> classifier = learner.Learn(inputs, outputs);


ConfusionMatrix cm = ConfusionMatrix.Estimate(classifier, inputs, outputs);

double error = cm.Error;  
double acc = cm.Accuracy; 
double kappa = cm.Kappa;  


bool y = classifier.Decide(inputs[0]); 

It is also possible to use the DecisionStump as a standalone classifier through the ThresholdLearning algorithm. An example is given below:

double[][] inputs =
{
    new double[] {  10, 42 },
    new double[] { 162, 96 },
    new double[] { 125, 20 },
    new double[] {  96,  6 },
    new double[] {   2, 73 },
    new double[] {  52, 51 },
    new double[] {  71, 49 },
};


bool[] outputs =
{
    false, false, true, true, false, false, true
};


var teacher = new ThresholdLearning();


DecisionStump classifier = teacher.Learn(inputs, outputs);


var cm = ConfusionMatrix.Estimate(classifier, inputs, outputs);

double error = cm.Error; 


bool y = classifier.Decide(new double[] { 71, 48 }); 

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