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BinaryConfusionMatrix (BigQuery API v2-rev20240727-2.0.0)











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com.google.api.services.bigquery.model
Class BinaryConfusionMatrix



java.lang.Object


java.util.AbstractMap<String,Object>


com.google.api.client.util.GenericData


com.google.api.client.json.GenericJson


com.google.api.services.bigquery.model.BinaryConfusionMatrix













All Implemented Interfaces:
Cloneable, Map<String,Object>




public final class BinaryConfusionMatrix
extends com.google.api.client.json.GenericJson
Confusion matrix for binary classification models.

  This is the Java data model class that specifies how to parse/serialize into the JSON that is
 transmitted over HTTP when working with the BigQuery API. For a detailed explanation see:
 https://developers.google.com/api-client-library/java/google-http-java-client/json
 

Author:
Google, Inc.












Nested Class Summary




Nested classes/interfaces inherited from class com.google.api.client.util.GenericData
com.google.api.client.util.GenericData.Flags





Nested classes/interfaces inherited from class java.util.AbstractMap
AbstractMap.SimpleEntry<K,V>, AbstractMap.SimpleImmutableEntry<K,V>





Nested classes/interfaces inherited from interface java.util.Map
Map.Entry<K,V>








Constructor Summary

Constructors 

Constructor and Description


BinaryConfusionMatrix() 









Method Summary

All Methods Instance Methods Concrete Methods 

Modifier and Type
Method and Description


BinaryConfusionMatrix
clone() 


Double
getAccuracy()
The fraction of predictions given the correct label.



Double
getF1Score()
The equally weighted average of recall and precision.



Long
getFalseNegatives()
Number of false samples predicted as false.



Long
getFalsePositives()
Number of false samples predicted as true.



Double
getPositiveClassThreshold()
Threshold value used when computing each of the following metric.



Double
getPrecision()
The fraction of actual positive predictions that had positive actual labels.



Double
getRecall()
The fraction of actual positive labels that were given a positive prediction.



Long
getTrueNegatives()
Number of true samples predicted as false.



Long
getTruePositives()
Number of true samples predicted as true.



BinaryConfusionMatrix
set(String fieldName,
   Object value) 


BinaryConfusionMatrix
setAccuracy(Double accuracy)
The fraction of predictions given the correct label.



BinaryConfusionMatrix
setF1Score(Double f1Score)
The equally weighted average of recall and precision.



BinaryConfusionMatrix
setFalseNegatives(Long falseNegatives)
Number of false samples predicted as false.



BinaryConfusionMatrix
setFalsePositives(Long falsePositives)
Number of false samples predicted as true.



BinaryConfusionMatrix
setPositiveClassThreshold(Double positiveClassThreshold)
Threshold value used when computing each of the following metric.



BinaryConfusionMatrix
setPrecision(Double precision)
The fraction of actual positive predictions that had positive actual labels.



BinaryConfusionMatrix
setRecall(Double recall)
The fraction of actual positive labels that were given a positive prediction.



BinaryConfusionMatrix
setTrueNegatives(Long trueNegatives)
Number of true samples predicted as false.



BinaryConfusionMatrix
setTruePositives(Long truePositives)
Number of true samples predicted as true.







Methods inherited from class com.google.api.client.json.GenericJson
getFactory, setFactory, toPrettyString, toString





Methods inherited from class com.google.api.client.util.GenericData
entrySet, equals, get, getClassInfo, getUnknownKeys, hashCode, put, putAll, remove, setUnknownKeys





Methods inherited from class java.util.AbstractMap
clear, containsKey, containsValue, isEmpty, keySet, size, values





Methods inherited from class java.lang.Object
finalize, getClass, notify, notifyAll, wait, wait, wait





Methods inherited from interface java.util.Map
compute, computeIfAbsent, computeIfPresent, forEach, getOrDefault, merge, putIfAbsent, remove, replace, replace, replaceAll














Constructor Detail





BinaryConfusionMatrix
public BinaryConfusionMatrix()









Method Detail





getAccuracy
public Double getAccuracy()
The fraction of predictions given the correct label.

Returns:
value or null for none








setAccuracy
public BinaryConfusionMatrix setAccuracy(Double accuracy)
The fraction of predictions given the correct label.

Parameters:
accuracy - accuracy or null for none








getF1Score
public Double getF1Score()
The equally weighted average of recall and precision.

Returns:
value or null for none








setF1Score
public BinaryConfusionMatrix setF1Score(Double f1Score)
The equally weighted average of recall and precision.

Parameters:
f1Score - f1Score or null for none








getFalseNegatives
public Long getFalseNegatives()
Number of false samples predicted as false.

Returns:
value or null for none








setFalseNegatives
public BinaryConfusionMatrix setFalseNegatives(Long falseNegatives)
Number of false samples predicted as false.

Parameters:
falseNegatives - falseNegatives or null for none








getFalsePositives
public Long getFalsePositives()
Number of false samples predicted as true.

Returns:
value or null for none








setFalsePositives
public BinaryConfusionMatrix setFalsePositives(Long falsePositives)
Number of false samples predicted as true.

Parameters:
falsePositives - falsePositives or null for none








getPositiveClassThreshold
public Double getPositiveClassThreshold()
Threshold value used when computing each of the following metric.

Returns:
value or null for none








setPositiveClassThreshold
public BinaryConfusionMatrix setPositiveClassThreshold(Double positiveClassThreshold)
Threshold value used when computing each of the following metric.

Parameters:
positiveClassThreshold - positiveClassThreshold or null for none








getPrecision
public Double getPrecision()
The fraction of actual positive predictions that had positive actual labels.

Returns:
value or null for none








setPrecision
public BinaryConfusionMatrix setPrecision(Double precision)
The fraction of actual positive predictions that had positive actual labels.

Parameters:
precision - precision or null for none








getRecall
public Double getRecall()
The fraction of actual positive labels that were given a positive prediction.

Returns:
value or null for none








setRecall
public BinaryConfusionMatrix setRecall(Double recall)
The fraction of actual positive labels that were given a positive prediction.

Parameters:
recall - recall or null for none








getTrueNegatives
public Long getTrueNegatives()
Number of true samples predicted as false.

Returns:
value or null for none








setTrueNegatives
public BinaryConfusionMatrix setTrueNegatives(Long trueNegatives)
Number of true samples predicted as false.

Parameters:
trueNegatives - trueNegatives or null for none








getTruePositives
public Long getTruePositives()
Number of true samples predicted as true.

Returns:
value or null for none








setTruePositives
public BinaryConfusionMatrix setTruePositives(Long truePositives)
Number of true samples predicted as true.

Parameters:
truePositives - truePositives or null for none








set
public BinaryConfusionMatrix set(String fieldName,
                                 Object value)

Overrides:
set in class com.google.api.client.json.GenericJson








clone
public BinaryConfusionMatrix clone()

Overrides:
clone in class com.google.api.client.json.GenericJson














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Copyright © 2011–2024 Google. All rights reserved.

Modifier and Type	Method and Description
`BinaryConfusionMatrix`	`clone()`
`Double`	`getAccuracy()` The fraction of predictions given the correct label.
`Double`	`getF1Score()` The equally weighted average of recall and precision.
`Long`	`getFalseNegatives()` Number of false samples predicted as false.
`Long`	`getFalsePositives()` Number of false samples predicted as true.
`Double`	`getPositiveClassThreshold()` Threshold value used when computing each of the following metric.
`Double`	`getPrecision()` The fraction of actual positive predictions that had positive actual labels.
`Double`	`getRecall()` The fraction of actual positive labels that were given a positive prediction.
`Long`	`getTrueNegatives()` Number of true samples predicted as false.
`Long`	`getTruePositives()` Number of true samples predicted as true.
`BinaryConfusionMatrix`	`set(String fieldName, Object value)`
`BinaryConfusionMatrix`	`setAccuracy(Double accuracy)` The fraction of predictions given the correct label.
`BinaryConfusionMatrix`	`setF1Score(Double f1Score)` The equally weighted average of recall and precision.
`BinaryConfusionMatrix`	`setFalseNegatives(Long falseNegatives)` Number of false samples predicted as false.
`BinaryConfusionMatrix`	`setFalsePositives(Long falsePositives)` Number of false samples predicted as true.
`BinaryConfusionMatrix`	`setPositiveClassThreshold(Double positiveClassThreshold)` Threshold value used when computing each of the following metric.
`BinaryConfusionMatrix`	`setPrecision(Double precision)` The fraction of actual positive predictions that had positive actual labels.
`BinaryConfusionMatrix`	`setRecall(Double recall)` The fraction of actual positive labels that were given a positive prediction.
`BinaryConfusionMatrix`	`setTrueNegatives(Long trueNegatives)` Number of true samples predicted as false.
`BinaryConfusionMatrix`	`setTruePositives(Long truePositives)` Number of true samples predicted as true.