ClassifierEvaluation (Tribuo 4.1.1 API)

Type Parameters:

T - The output type.

All Superinterfaces:

Evaluation<T>, com.oracle.labs.mlrg.olcut.provenance.Provenancable<EvaluationProvenance>

All Known Subinterfaces:

LabelEvaluation, MultiLabelEvaluation

All Known Implementing Classes:

MultiLabelEvaluationImpl
```
public interface ClassifierEvaluation<T extends Classifiable<T>>
extends Evaluation<T>
```
Defines methods that calculate classification performance, used for both multi-class and multi-label classification.

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method and Description
`double`	`balancedErrorRate()` Returns the balanced error rate, i.e., the mean of the per label recalls.
`double`	`confusion(T predicted, T truth)` Returns the number of times label `truth` was predicted as label `predicted`.
`double`	`f1(T label)` Returns the F_1 score, i.e., the harmonic mean of the precision and recall.
`double`	`fn()` Returns the micro averaged number of false negatives.
`double`	`fn(T label)` Returns the number of false negatives, i.e., the number of times the true label was incorrectly predicted as another label.
`double`	`fp()` Returns the micro average of the number of false positives across all the labels, i.e., the total number of false positives.
`double`	`fp(T label)` Returns the number of false positives, i.e., the number of times this label was predicted but it was not the true label..
`ConfusionMatrix<T>`	`getConfusionMatrix()` Returns the underlying confusion matrix.
`double`	`macroAveragedF1()` Returns the macro averaged F_1 across all the labels.
`double`	`macroAveragedPrecision()` Returns the macro averaged precision.
`double`	`macroAveragedRecall()` Returns the macro averaged recall.
`double`	`macroFN()` Returns the macro averaged number of false negatives.
`double`	`macroFP()` Returns the macro averaged number of false positives, averaged across the labels.
`double`	`macroTN()` Returns the macro averaged number of true negatives.
`double`	`macroTP()` Returns the macro averaged number of true positives, averaged across the labels.
`double`	`microAveragedF1()` Returns the micro averaged F_1 across all labels.
`double`	`microAveragedPrecision()` Returns the micro averaged precision.
`double`	`microAveragedRecall()` Returns the micro averaged recall.
`double`	`precision(T label)` Returns the precision of this label, i.e., the number of true positives divided by the number of true positives plus false positives.
`double`	`recall(T label)` Returns the recall of this label, i.e., the number of true positives divided by the number of true positives plus false negatives.
`double`	`tn()` Returns the total number of true negatives.
`double`	`tn(T label)` Returns the number of true negatives for that label, i.e., the number of times it wasn't predicted, and was not the true label.
`double`	`tp()` Returns the micro average of the number of true positives across all the labels, i.e., the total number of true positives.
`double`	`tp(T label)` Returns the number of true positives, i.e., the number of times the label was correctly predicted.

Methods inherited from interface org.tribuo.evaluation.Evaluation
asMap, get, getPredictions

Methods inherited from interface com.oracle.labs.mlrg.olcut.provenance.Provenancable
getProvenance

- Method Detail
  - confusion
```
double confusion(T predicted,
                 T truth)
```
    Returns the number of times label truth was predicted as label predicted.
    
    Parameters:
    
    predicted - The predicted label.
    
    truth - The true label.
    
    Returns:
    
    The number of times the predicted label was returned for the true label.
  - tp
```
double tp(T label)
```
    Returns the number of true positives, i.e., the number of times the label was correctly predicted.
    
    Parameters:
    
    label - The label to calculate.
    
    Returns:
    
    The number of true positives for that label.
  - tp
```
double tp()
```
    Returns the micro average of the number of true positives across all the labels, i.e., the total number of true positives.
    
    Returns:
    
    The micro averaged number of true positives.
  - macroTP
```
double macroTP()
```
    Returns the macro averaged number of true positives, averaged across the labels.
    
    Returns:
    
    The macro averaged number of true positives.
  - fp
```
double fp(T label)
```
    Returns the number of false positives, i.e., the number of times this label was predicted but it was not the true label..
    
    Parameters:
    
    label - the label to calculate.
    
    Returns:
    
    The number of false positives for that label.
  - fp
```
double fp()
```
    Returns the micro average of the number of false positives across all the labels, i.e., the total number of false positives.
    
    Returns:
    
    The micro averaged number of false positives.
  - macroFP
```
double macroFP()
```
    Returns the macro averaged number of false positives, averaged across the labels.
    
    Returns:
    
    The macro averaged number of false positives.
  - tn
```
double tn(T label)
```
    Returns the number of true negatives for that label, i.e., the number of times it wasn't predicted, and was not the true label.
    
    Parameters:
    
    label - The label to use.
    
    Returns:
    
    the number of true negatives.
  - tn
```
double tn()
```
    Returns the total number of true negatives. This isn't very useful in multiclass problems.
    
    Returns:
    
    The number of true negatives.
  - macroTN
```
double macroTN()
```
    Returns the macro averaged number of true negatives.
    
    Returns:
    
    The macro averaged number of true negatives.
  - fn
```
double fn(T label)
```
    Returns the number of false negatives, i.e., the number of times the true label was incorrectly predicted as another label.
    
    Parameters:
    
    label - The true label.
    
    Returns:
    
    The number of false negatives.
  - fn
```
double fn()
```
    Returns the micro averaged number of false negatives.
    
    Returns:
    
    The micro averaged number of false negatives.
  - macroFN
```
double macroFN()
```
    Returns the macro averaged number of false negatives.
    
    Returns:
    
    The macro averaged number of false negatives.
  - precision
```
double precision(T label)
```
    Returns the precision of this label, i.e., the number of true positives divided by the number of true positives plus false positives.
    
    Parameters:
    
    label - The label.
    
    Returns:
    
    The precision.
  - microAveragedPrecision
```
double microAveragedPrecision()
```
    Returns the micro averaged precision.
    
    Returns:
    
    The micro averaged precision.
  - macroAveragedPrecision
```
double macroAveragedPrecision()
```
    Returns the macro averaged precision.
    
    Returns:
    
    The macro averaged precision.
  - recall
```
double recall(T label)
```
    Returns the recall of this label, i.e., the number of true positives divided by the number of true positives plus false negatives.
    
    Parameters:
    
    label - The label.
    
    Returns:
    
    The recall.
  - microAveragedRecall
```
double microAveragedRecall()
```
    Returns the micro averaged recall.
    
    Returns:
    
    The micro averaged recall.
  - macroAveragedRecall
```
double macroAveragedRecall()
```
    Returns the macro averaged recall.
    
    Returns:
    
    The macro averaged recall.
  - f1
```
double f1(T label)
```
    Returns the F_1 score, i.e., the harmonic mean of the precision and recall.
    
    Parameters:
    
    label - The label.
    
    Returns:
    
    The F_1 score.
  - microAveragedF1
```
double microAveragedF1()
```
    Returns the micro averaged F_1 across all labels.
    
    Returns:
    
    The F_1 score.
  - macroAveragedF1
```
double macroAveragedF1()
```
    Returns the macro averaged F_1 across all the labels.
    
    Returns:
    
    The F_1 score.
  - balancedErrorRate
```
double balancedErrorRate()
```
    Returns the balanced error rate, i.e., the mean of the per label recalls.
    
    Returns:
    
    The balanced error rate.
  - getConfusionMatrix
```
ConfusionMatrix<T> getConfusionMatrix()
```
    Returns the underlying confusion matrix.
    
    Returns:
    
    The confusion matrix.

Interface ClassifierEvaluation<T extends Classifiable<T>>

Method Summary

Methods inherited from interface org.tribuo.evaluation.Evaluation

Methods inherited from interface com.oracle.labs.mlrg.olcut.provenance.Provenancable

Method Detail

confusion

tp

tp

macroTP

fp

fp

macroFP

tn

tn

macroTN

fn

fn

macroFN

precision

microAveragedPrecision

macroAveragedPrecision

recall

microAveragedRecall

macroAveragedRecall

f1

microAveragedF1

macroAveragedF1

balancedErrorRate

getConfusionMatrix