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ThresholdLearning Class
Learning algorithm for DecisionStumps.
Inheritance Hierarchy
SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.MachineLearning.Boosting.LearnersThresholdLearning

Namespace:  Accord.MachineLearning.Boosting.Learners
Assembly:  Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0
Syntax
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public class ThresholdLearning : ParallelLearningBase, 
	ISupervisedBinaryLearning<DecisionStump, double[]>, ISupervisedMulticlassLearning<DecisionStump, double[]>, 
	ISupervisedMultilabelLearning<DecisionStump, double[]>, ISupervisedLearning<DecisionStump, double[], int[]>, 
	ISupervisedLearning<DecisionStump, double[], bool[]>, ISupervisedLearning<DecisionStump, double[], int>, 
	ISupervisedLearning<DecisionStump, double[], bool>
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The ThresholdLearning type exposes the following members.

Constructors
  NameDescription
Public methodThresholdLearning
Initializes a new instance of the ThresholdLearning class
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Properties
  NameDescription
Public propertyModel
Gets or sets the model being trained.
Public propertyParallelOptions
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.)
Public propertyToken
Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.)
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Methods
  NameDescription
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodLearn(Double, Boolean, Double)
Learns a model that can map the given inputs to the given outputs.
Public methodLearn(Double, Boolean, Double)
Learns a model that can map the given inputs to the given outputs.
Public methodLearn(Double, Int32, Double)
Learns a model that can map the given inputs to the given outputs.
Public methodLearn(Double, Int32, Double)
Learns a model that can map the given inputs to the given outputs.
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Extension Methods
  NameDescription
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodTo(Type)Overloaded.
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.
(Defined by ExtensionMethods.)
Public Extension MethodToTOverloaded.
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.
(Defined by ExtensionMethods.)
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Examples

The ThresholdLearning algorithm is mostly intended to be used to create DecisionStump weak classifiers 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:

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// Let's say we want to classify the following 2-dimensional 
// data samples into 2 possible classes, either true or false:
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 },
};

// And those are their associated class labels
bool[] outputs =
{
    false, false, true, true, false, false, true
};

// We can create an AdaBoost algorithm as:
var learner = new AdaBoost<DecisionStump>()
{
    Learner = (p) => new ThresholdLearning(),

    // Train until:
    MaxIterations = 5,
    Tolerance = 1e-3
};

// Now, we can use the Learn method to learn a boosted classifier
Boost<DecisionStump> classifier = learner.Learn(inputs, outputs);

// And we can test its performance using (error should be 0):
ConfusionMatrix cm = ConfusionMatrix.Estimate(classifier, inputs, outputs);

double error = cm.Error;  // should be 0.0
double acc = cm.Accuracy; // should be 1.0
double kappa = cm.Kappa;  // should be 1.0

// And compute a decision for a single data point using:
bool y = classifier.Decide(inputs[0]); // result should false

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

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// Let's say we want to classify the following 2-dimensional 
// data samples into 2 possible classes, either true or false:
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 },
};

// And those are their associated class labels
bool[] outputs =
{
    false, false, true, true, false, false, true
};

// We create a learning algorithm as:
var teacher = new ThresholdLearning();

// Now, we can use the Learn method to learn a classifier:
DecisionStump classifier = teacher.Learn(inputs, outputs);

// Now, we can check how good it is using a confusion matrix:
var cm = ConfusionMatrix.Estimate(classifier, inputs, outputs);

double error = cm.Error; // should be ~0.14

// We can also compute the model outputs for new samples using
bool y = classifier.Decide(new double[] { 71, 48 }); // should be false
See Also