| BinaryClassification Class |
Inheritance HierarchyNamespace: Imsl.DataMining.Neural
Assembly: ImslCS (in ImslCS.dll) Version: 6.5.2.0
SyntaxThe BinaryClassification type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | BinaryClassification |
Creates a binary classifier.
|
Methods| Name | Description | |
|---|---|---|
| ComputeStatistics |
Computes the classification error statistics for the supplied network patterns
and their associated classifications.
|
| Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) |
 | Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.) |
| GetHashCode | Serves as a hash function for a particular type. (Inherited from Object.) |
| GetType | Gets the Type of the current instance. (Inherited from Object.) |
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) |
| PredictedClass |
Calculates the classification probablities for the input
pattern x, and returns either 0 or 1 identifying the class with the
highest probability.
|
| Probabilities |
Returns classification probabilities for the input pattern x.
|
| ToString | Returns a string that represents the current object. (Inherited from Object.) |
| Train |
Trains the classification neural network using supplied trainer and patterns.
|
Properties| Name | Description | |
|---|---|---|
 | Error |
Returns the error function for use by QuasiNewtonTrainer
for training a binary classification network.
|
| Network |
The network being used for classification.
|
RemarksUses a FeedForwardNetwork to solve binary classification problems.
In these problems, the target output for the network is the probability that
the pattern falls into one of two classes. The first class, 
,
is usually equal to one and the second class, 
equal to zero. These probabilities are then used to assign patterns to one
of the two classes. Typical applications include determining whether a
credit applicant is a good or bad credit risk, and determining whether a
person should or should not receive a particular treatment based upon their
physical, clinical and laboratory information. This class signals that network
training will minimize the binary cross-entropy error, and that network output
is the probability that the pattern belongs to the first class,
. Which is calculated by applying the logistic
activation function to the potential of the single output. The probability
for the second class is calculated by 
.
See Also