target.apidocs.com.google.api.services.bigquery.model.EvaluationMetrics.html Maven / Gradle / Ivy

Go to download

Show more of this group Show more artifacts with this name
Show all versions of google-api-services-bigquery Show documentation

There is a newer version: v2-rev20241027-2.0.0

Show newest version







EvaluationMetrics (BigQuery API v2-rev20240727-2.0.0)











Skip navigation links




Overview
Package
Class
Use
Tree
Deprecated
Index
Help




Prev Class
Next Class


Frames
No Frames


All Classes






Summary: 
Nested | 
Field | 
Constr | 
Method


Detail: 
Field | 
Constr | 
Method








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



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.EvaluationMetrics













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




public final class EvaluationMetrics
extends com.google.api.client.json.GenericJson
Evaluation metrics of a model. These are either computed on all training data or just the eval
 data based on whether eval data was used during training. These are not present for imported
 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


EvaluationMetrics() 









Method Summary

All Methods Instance Methods Concrete Methods 

Modifier and Type
Method and Description


EvaluationMetrics
clone() 


ArimaForecastingMetrics
getArimaForecastingMetrics()
Populated for ARIMA models.



BinaryClassificationMetrics
getBinaryClassificationMetrics()
Populated for binary classification/classifier models.



ClusteringMetrics
getClusteringMetrics()
Populated for clustering models.



DimensionalityReductionMetrics
getDimensionalityReductionMetrics()
Evaluation metrics when the model is a dimensionality reduction model, which currently includes
 PCA.



MultiClassClassificationMetrics
getMultiClassClassificationMetrics()
Populated for multi-class classification/classifier models.



RankingMetrics
getRankingMetrics()
Populated for implicit feedback type matrix factorization models.



RegressionMetrics
getRegressionMetrics()
Populated for regression models and explicit feedback type matrix factorization models.



EvaluationMetrics
set(String fieldName,
   Object value) 


EvaluationMetrics
setArimaForecastingMetrics(ArimaForecastingMetrics arimaForecastingMetrics)
Populated for ARIMA models.



EvaluationMetrics
setBinaryClassificationMetrics(BinaryClassificationMetrics binaryClassificationMetrics)
Populated for binary classification/classifier models.



EvaluationMetrics
setClusteringMetrics(ClusteringMetrics clusteringMetrics)
Populated for clustering models.



EvaluationMetrics
setDimensionalityReductionMetrics(DimensionalityReductionMetrics dimensionalityReductionMetrics)
Evaluation metrics when the model is a dimensionality reduction model, which currently includes
 PCA.



EvaluationMetrics
setMultiClassClassificationMetrics(MultiClassClassificationMetrics multiClassClassificationMetrics)
Populated for multi-class classification/classifier models.



EvaluationMetrics
setRankingMetrics(RankingMetrics rankingMetrics)
Populated for implicit feedback type matrix factorization models.



EvaluationMetrics
setRegressionMetrics(RegressionMetrics regressionMetrics)
Populated for regression models and explicit feedback type matrix factorization models.







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





EvaluationMetrics
public EvaluationMetrics()









Method Detail





getArimaForecastingMetrics
public ArimaForecastingMetrics getArimaForecastingMetrics()
Populated for ARIMA models.

Returns:
value or null for none








setArimaForecastingMetrics
public EvaluationMetrics setArimaForecastingMetrics(ArimaForecastingMetrics arimaForecastingMetrics)
Populated for ARIMA models.

Parameters:
arimaForecastingMetrics - arimaForecastingMetrics or null for none








getBinaryClassificationMetrics
public BinaryClassificationMetrics getBinaryClassificationMetrics()
Populated for binary classification/classifier models.

Returns:
value or null for none








setBinaryClassificationMetrics
public EvaluationMetrics setBinaryClassificationMetrics(BinaryClassificationMetrics binaryClassificationMetrics)
Populated for binary classification/classifier models.

Parameters:
binaryClassificationMetrics - binaryClassificationMetrics or null for none








getClusteringMetrics
public ClusteringMetrics getClusteringMetrics()
Populated for clustering models.

Returns:
value or null for none








setClusteringMetrics
public EvaluationMetrics setClusteringMetrics(ClusteringMetrics clusteringMetrics)
Populated for clustering models.

Parameters:
clusteringMetrics - clusteringMetrics or null for none








getDimensionalityReductionMetrics
public DimensionalityReductionMetrics getDimensionalityReductionMetrics()
Evaluation metrics when the model is a dimensionality reduction model, which currently includes
 PCA.

Returns:
value or null for none








setDimensionalityReductionMetrics
public EvaluationMetrics setDimensionalityReductionMetrics(DimensionalityReductionMetrics dimensionalityReductionMetrics)
Evaluation metrics when the model is a dimensionality reduction model, which currently includes
 PCA.

Parameters:
dimensionalityReductionMetrics - dimensionalityReductionMetrics or null for none








getMultiClassClassificationMetrics
public MultiClassClassificationMetrics getMultiClassClassificationMetrics()
Populated for multi-class classification/classifier models.

Returns:
value or null for none








setMultiClassClassificationMetrics
public EvaluationMetrics setMultiClassClassificationMetrics(MultiClassClassificationMetrics multiClassClassificationMetrics)
Populated for multi-class classification/classifier models.

Parameters:
multiClassClassificationMetrics - multiClassClassificationMetrics or null for none








getRankingMetrics
public RankingMetrics getRankingMetrics()
Populated for implicit feedback type matrix factorization models.

Returns:
value or null for none








setRankingMetrics
public EvaluationMetrics setRankingMetrics(RankingMetrics rankingMetrics)
Populated for implicit feedback type matrix factorization models.

Parameters:
rankingMetrics - rankingMetrics or null for none








getRegressionMetrics
public RegressionMetrics getRegressionMetrics()
Populated for regression models and explicit feedback type matrix factorization models.

Returns:
value or null for none








setRegressionMetrics
public EvaluationMetrics setRegressionMetrics(RegressionMetrics regressionMetrics)
Populated for regression models and explicit feedback type matrix factorization models.

Parameters:
regressionMetrics - regressionMetrics or null for none








set
public EvaluationMetrics set(String fieldName,
                             Object value)

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








clone
public EvaluationMetrics clone()

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














Skip navigation links




Overview
Package
Class
Use
Tree
Deprecated
Index
Help




Prev Class
Next Class


Frames
No Frames


All Classes






Summary: 
Nested | 
Field | 
Constr | 
Method


Detail: 
Field | 
Constr | 
Method






Copyright © 2011–2024 Google. All rights reserved.

Modifier and Type	Method and Description
`EvaluationMetrics`	`clone()`
`ArimaForecastingMetrics`	`getArimaForecastingMetrics()` Populated for ARIMA models.
`BinaryClassificationMetrics`	`getBinaryClassificationMetrics()` Populated for binary classification/classifier models.
`ClusteringMetrics`	`getClusteringMetrics()` Populated for clustering models.
`DimensionalityReductionMetrics`	`getDimensionalityReductionMetrics()` Evaluation metrics when the model is a dimensionality reduction model, which currently includes PCA.
`MultiClassClassificationMetrics`	`getMultiClassClassificationMetrics()` Populated for multi-class classification/classifier models.
`RankingMetrics`	`getRankingMetrics()` Populated for implicit feedback type matrix factorization models.
`RegressionMetrics`	`getRegressionMetrics()` Populated for regression models and explicit feedback type matrix factorization models.
`EvaluationMetrics`	`set(String fieldName, Object value)`
`EvaluationMetrics`	`setArimaForecastingMetrics(ArimaForecastingMetrics arimaForecastingMetrics)` Populated for ARIMA models.
`EvaluationMetrics`	`setBinaryClassificationMetrics(BinaryClassificationMetrics binaryClassificationMetrics)` Populated for binary classification/classifier models.
`EvaluationMetrics`	`setClusteringMetrics(ClusteringMetrics clusteringMetrics)` Populated for clustering models.
`EvaluationMetrics`	`setDimensionalityReductionMetrics(DimensionalityReductionMetrics dimensionalityReductionMetrics)` Evaluation metrics when the model is a dimensionality reduction model, which currently includes PCA.
`EvaluationMetrics`	`setMultiClassClassificationMetrics(MultiClassClassificationMetrics multiClassClassificationMetrics)` Populated for multi-class classification/classifier models.
`EvaluationMetrics`	`setRankingMetrics(RankingMetrics rankingMetrics)` Populated for implicit feedback type matrix factorization models.
`EvaluationMetrics`	`setRegressionMetrics(RegressionMetrics regressionMetrics)` Populated for regression models and explicit feedback type matrix factorization models.