com.google.api.services.prediction.model

Class Insert2.ModelInfo

java.lang.Object

java.util.AbstractMap<String,Object>

com.google.api.client.util.GenericData

com.google.api.client.json.GenericJson

com.google.api.services.prediction.model.Insert2.ModelInfo

All Implemented Interfaces:

Cloneable, Map<String,Object>

Enclosing class:

Insert2

public static final class Insert2.ModelInfo
extends GenericJson

Model metadata.

Nested Class Summary

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

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
Insert2.ModelInfo()

Method Summary

Methods

Modifier and Type	Method and Description
Insert2.ModelInfo	clone()
String	getClassificationAccuracy() A number between 0.0 and 1.0, where 1.0 is 100% accurate.
String	getClassWeightedAccuracy() Estimated accuracy of model taking utility weights into account (Categorical models only).
String	getMeanSquaredError() An estimated mean squared error.
String	getModelType() Type of predictive model (CLASSIFICATION or REGRESSION).
Long	getNumberInstances() Number of valid data instances used in the trained model.
Long	getNumberLabels() Number of class labels in the trained model (Categorical models only).
Insert2.ModelInfo	set(String fieldName, Object value)
Insert2.ModelInfo	setClassificationAccuracy(String classificationAccuracy) A number between 0.0 and 1.0, where 1.0 is 100% accurate.
Insert2.ModelInfo	setClassWeightedAccuracy(String classWeightedAccuracy) Estimated accuracy of model taking utility weights into account (Categorical models only).
Insert2.ModelInfo	setMeanSquaredError(String meanSquaredError) An estimated mean squared error.
Insert2.ModelInfo	setModelType(String modelType) Type of predictive model (CLASSIFICATION or REGRESSION).
Insert2.ModelInfo	setNumberInstances(Long numberInstances) Number of valid data instances used in the trained model.
Insert2.ModelInfo	setNumberLabels(Long numberLabels) Number of class labels in the trained model (Categorical models only).

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, get, getClassInfo, getUnknownKeys, put, putAll, remove, setUnknownKeys

Methods inherited from class java.util.AbstractMap

clear, containsKey, containsValue, equals, hashCode, isEmpty, keySet, size, values

Methods inherited from class java.lang.Object

finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

Insert2.ModelInfo

public Insert2.ModelInfo()

Method Detail

getClassWeightedAccuracy

public String getClassWeightedAccuracy()

Estimated accuracy of model taking utility weights into account (Categorical models only).

Returns:

value or null for none

setClassWeightedAccuracy

public Insert2.ModelInfo setClassWeightedAccuracy(String classWeightedAccuracy)

Estimated accuracy of model taking utility weights into account (Categorical models only).

Parameters:

classWeightedAccuracy - classWeightedAccuracy or null for none

getClassificationAccuracy

public String getClassificationAccuracy()

A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an estimate, based on the amount and quality of the training data, of the estimated prediction accuracy. You can use this is a guide to decide whether the results are accurate enough for your needs. This estimate will be more reliable if your real input data is similar to your training data (Categorical models only).

Returns:

value or null for none

setClassificationAccuracy

public Insert2.ModelInfo setClassificationAccuracy(String classificationAccuracy)

A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an estimate, based on the amount and quality of the training data, of the estimated prediction accuracy. You can use this is a guide to decide whether the results are accurate enough for your needs. This estimate will be more reliable if your real input data is similar to your training data (Categorical models only).

Parameters:

classificationAccuracy - classificationAccuracy or null for none

getMeanSquaredError

public String getMeanSquaredError()

An estimated mean squared error. The can be used to measure the quality of the predicted model (Regression models only).

Returns:

value or null for none

setMeanSquaredError

public Insert2.ModelInfo setMeanSquaredError(String meanSquaredError)

An estimated mean squared error. The can be used to measure the quality of the predicted model (Regression models only).

Parameters:

meanSquaredError - meanSquaredError or null for none

getModelType

public String getModelType()

Type of predictive model (CLASSIFICATION or REGRESSION).

Returns:

value or null for none

setModelType

public Insert2.ModelInfo setModelType(String modelType)

Type of predictive model (CLASSIFICATION or REGRESSION).

Parameters:

modelType - modelType or null for none

getNumberInstances

public Long getNumberInstances()

Number of valid data instances used in the trained model.

Returns:

value or null for none

setNumberInstances

public Insert2.ModelInfo setNumberInstances(Long numberInstances)

Number of valid data instances used in the trained model.

Parameters:

numberInstances - numberInstances or null for none

getNumberLabels

public Long getNumberLabels()

Number of class labels in the trained model (Categorical models only).

Returns:

value or null for none

setNumberLabels

public Insert2.ModelInfo setNumberLabels(Long numberLabels)

Number of class labels in the trained model (Categorical models only).

Parameters:

numberLabels - numberLabels or null for none

set

public Insert2.ModelInfo set(String fieldName,
                    Object value)

Overrides:

set in class GenericJson

clone

public Insert2.ModelInfo clone()

Overrides:

clone in class GenericJson