Package gov.sandia.cognition.learning.performance

Provides performance measures.

See:
          Description

Interface Summary
PerformanceEvaluator<ObjectType,DataType,ResultType> The PerformanceEvaluator class defines the functionality of some object with regards to some data.
SupervisedPerformanceEvaluator<InputType,TargetType,EstimateType,ResultType> The SupervisedPerformanceEvaluator interface extends the PerformanceEvaluator interface for performance evaluations of supervised machine learning algorithms where the target type is evaluated against the estimated type produced by the evaluator.
 

Class Summary
AbstractSupervisedPerformanceEvaluator<InputType,TargetType,EstimateType,ResultType> The AbstractSupervisedPerformanceEvaluator class contains an abstract implementation of the SupervisedPerformanceEvaluator class.
MeanAbsoluteErrorEvaluator<InputType> The MeanAbsoluteError class implements a method for computing the performance of a supervised learner for a scalar function by the mean absolute value between the target and estimated outputs.
MeanSquaredErrorEvaluator<InputType> The MeanSquaredError class implements the method for computing the performance of a supervised learner for a scalar function by the mean squared between the target and estimated outputs.
MeanZeroOneErrorEvaluator<InputType,DataType> The MeanZeroOneErrorEvaluator class implements a method for computing the performance of a supervised learner by the mean number of incorrect values between the target and estimated outputs.
RootMeanSquaredErrorEvaluator<InputType> The RootMeanSquaredErrorEvaluator class implements a method for computing the performance of a supervised learner for a scalar function by the root mean squared error (RMSE or RSE) between the target and estimated outputs.
 

Package gov.sandia.cognition.learning.performance Description

Provides performance measures. These are typically used for measuring the performance of the output of a learning algorithm.

Since:
2.0
Author:
Justin Basilico