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AggregateClassificationMetrics (BigQuery API v2-rev20240727-2.0.0)











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



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.AggregateClassificationMetrics













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




public final class AggregateClassificationMetrics
extends com.google.api.client.json.GenericJson
Aggregate metrics for classification/classifier models. For multi-class models, the metrics are
 either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each
 label and then an unweighted average is taken of those values. When micro-averaged, the metric is
 calculated globally by counting the total number of correctly predicted rows.

  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


AggregateClassificationMetrics() 









Method Summary

All Methods Instance Methods Concrete Methods 

Modifier and Type
Method and Description


AggregateClassificationMetrics
clone() 


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



Double
getF1Score()
The F1 score is an average of recall and precision.



Double
getLogLoss()
Logarithmic Loss.



Double
getPrecision()
Precision is the fraction of actual positive predictions that had positive actual labels.



Double
getRecall()
Recall is the fraction of actual positive labels that were given a positive prediction.



Double
getRocAuc()
Area Under a ROC Curve.



Double
getThreshold()
Threshold at which the metrics are computed.



AggregateClassificationMetrics
set(String fieldName,
   Object value) 


AggregateClassificationMetrics
setAccuracy(Double accuracy)
Accuracy is the fraction of predictions given the correct label.



AggregateClassificationMetrics
setF1Score(Double f1Score)
The F1 score is an average of recall and precision.



AggregateClassificationMetrics
setLogLoss(Double logLoss)
Logarithmic Loss.



AggregateClassificationMetrics
setPrecision(Double precision)
Precision is the fraction of actual positive predictions that had positive actual labels.



AggregateClassificationMetrics
setRecall(Double recall)
Recall is the fraction of actual positive labels that were given a positive prediction.



AggregateClassificationMetrics
setRocAuc(Double rocAuc)
Area Under a ROC Curve.



AggregateClassificationMetrics
setThreshold(Double threshold)
Threshold at which the metrics are computed.







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





AggregateClassificationMetrics
public AggregateClassificationMetrics()









Method Detail





getAccuracy
public Double getAccuracy()
Accuracy is the fraction of predictions given the correct label. For multiclass this is a
 micro-averaged metric.

Returns:
value or null for none








setAccuracy
public AggregateClassificationMetrics setAccuracy(Double accuracy)
Accuracy is the fraction of predictions given the correct label. For multiclass this is a
 micro-averaged metric.

Parameters:
accuracy - accuracy or null for none








getF1Score
public Double getF1Score()
The F1 score is an average of recall and precision. For multiclass this is a macro-averaged
 metric.

Returns:
value or null for none








setF1Score
public AggregateClassificationMetrics setF1Score(Double f1Score)
The F1 score is an average of recall and precision. For multiclass this is a macro-averaged
 metric.

Parameters:
f1Score - f1Score or null for none








getLogLoss
public Double getLogLoss()
Logarithmic Loss. For multiclass this is a macro-averaged metric.

Returns:
value or null for none








setLogLoss
public AggregateClassificationMetrics setLogLoss(Double logLoss)
Logarithmic Loss. For multiclass this is a macro-averaged metric.

Parameters:
logLoss - logLoss or null for none








getPrecision
public Double getPrecision()
Precision is the fraction of actual positive predictions that had positive actual labels. For
 multiclass this is a macro-averaged metric treating each class as a binary classifier.

Returns:
value or null for none








setPrecision
public AggregateClassificationMetrics setPrecision(Double precision)
Precision is the fraction of actual positive predictions that had positive actual labels. For
 multiclass this is a macro-averaged metric treating each class as a binary classifier.

Parameters:
precision - precision or null for none








getRecall
public Double getRecall()
Recall is the fraction of actual positive labels that were given a positive prediction. For
 multiclass this is a macro-averaged metric.

Returns:
value or null for none








setRecall
public AggregateClassificationMetrics setRecall(Double recall)
Recall is the fraction of actual positive labels that were given a positive prediction. For
 multiclass this is a macro-averaged metric.

Parameters:
recall - recall or null for none








getRocAuc
public Double getRocAuc()
Area Under a ROC Curve. For multiclass this is a macro-averaged metric.

Returns:
value or null for none








setRocAuc
public AggregateClassificationMetrics setRocAuc(Double rocAuc)
Area Under a ROC Curve. For multiclass this is a macro-averaged metric.

Parameters:
rocAuc - rocAuc or null for none








getThreshold
public Double getThreshold()
Threshold at which the metrics are computed. For binary classification models this is the
 positive class threshold. For multi-class classfication models this is the confidence
 threshold.

Returns:
value or null for none








setThreshold
public AggregateClassificationMetrics setThreshold(Double threshold)
Threshold at which the metrics are computed. For binary classification models this is the
 positive class threshold. For multi-class classfication models this is the confidence
 threshold.

Parameters:
threshold - threshold or null for none








set
public AggregateClassificationMetrics set(String fieldName,
                                          Object value)

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








clone
public AggregateClassificationMetrics 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
`AggregateClassificationMetrics`	`clone()`
`Double`	`getAccuracy()` Accuracy is the fraction of predictions given the correct label.
`Double`	`getF1Score()` The F1 score is an average of recall and precision.
`Double`	`getLogLoss()` Logarithmic Loss.
`Double`	`getPrecision()` Precision is the fraction of actual positive predictions that had positive actual labels.
`Double`	`getRecall()` Recall is the fraction of actual positive labels that were given a positive prediction.
`Double`	`getRocAuc()` Area Under a ROC Curve.
`Double`	`getThreshold()` Threshold at which the metrics are computed.
`AggregateClassificationMetrics`	`set(String fieldName, Object value)`
`AggregateClassificationMetrics`	`setAccuracy(Double accuracy)` Accuracy is the fraction of predictions given the correct label.
`AggregateClassificationMetrics`	`setF1Score(Double f1Score)` The F1 score is an average of recall and precision.
`AggregateClassificationMetrics`	`setLogLoss(Double logLoss)` Logarithmic Loss.
`AggregateClassificationMetrics`	`setPrecision(Double precision)` Precision is the fraction of actual positive predictions that had positive actual labels.
`AggregateClassificationMetrics`	`setRecall(Double recall)` Recall is the fraction of actual positive labels that were given a positive prediction.
`AggregateClassificationMetrics`	`setRocAuc(Double rocAuc)` Area Under a ROC Curve.
`AggregateClassificationMetrics`	`setThreshold(Double threshold)` Threshold at which the metrics are computed.