io.datahubproject.openapi.generated

Class MLModelFactors

java.lang.Object

io.datahubproject.openapi.generated.MLModelFactors

@Validated
 @Generated(value="io.swagger.codegen.v3.generators.java.SpringCodegen",
           date="2022-09-21T05:34:26.783Z[Etc/UTC]")
public class MLModelFactors
extends java.lang.Object

Factors affecting the performance of the MLModel.

Constructor Summary

Constructors

Constructor and Description
MLModelFactors()

Method Summary

Modifier and Type	Method and Description
MLModelFactors	addEnvironmentItem(java.lang.String environmentItem)
MLModelFactors	addGroupsItem(java.lang.String groupsItem)
MLModelFactors	addInstrumentationItem(java.lang.String instrumentationItem)
MLModelFactors	environment(java.util.List<java.lang.String> environment)
boolean	equals(java.lang.Object o)
java.util.List<java.lang.String>	getEnvironment() A further factor affecting MLModel performance is the environment in which it is deployed.
java.util.List<java.lang.String>	getGroups() Groups refers to distinct categories with similar characteristics that are present in the evaluation data instances.
java.util.List<java.lang.String>	getInstrumentation() The performance of a MLModel can vary depending on what instruments were used to capture the input to the MLModel.
MLModelFactors	groups(java.util.List<java.lang.String> groups)
int	hashCode()
MLModelFactors	instrumentation(java.util.List<java.lang.String> instrumentation)
void	setEnvironment(java.util.List<java.lang.String> environment)
void	setGroups(java.util.List<java.lang.String> groups)
void	setInstrumentation(java.util.List<java.lang.String> instrumentation)
java.lang.String	toString()

Methods inherited from class java.lang.Object

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

Constructor Detail

MLModelFactors

public MLModelFactors()

Method Detail

groups

public MLModelFactors groups(java.util.List<java.lang.String> groups)

addGroupsItem

public MLModelFactors addGroupsItem(java.lang.String groupsItem)

getGroups

public java.util.List<java.lang.String> getGroups()

Groups refers to distinct categories with similar characteristics that are present in the evaluation data instances. For human-centric machine learning MLModels, groups are people who share one or multiple characteristics.

Returns:

groups

setGroups

public void setGroups(java.util.List<java.lang.String> groups)

instrumentation

public MLModelFactors instrumentation(java.util.List<java.lang.String> instrumentation)

addInstrumentationItem

public MLModelFactors addInstrumentationItem(java.lang.String instrumentationItem)

getInstrumentation

public java.util.List<java.lang.String> getInstrumentation()

The performance of a MLModel can vary depending on what instruments were used to capture the input to the MLModel. For example, a face detection model may perform differently depending on the camera’s hardware and software, including lens, image stabilization, high dynamic range techniques, and background blurring for portrait mode.

Returns:

instrumentation

setInstrumentation

public void setInstrumentation(java.util.List<java.lang.String> instrumentation)

environment

public MLModelFactors environment(java.util.List<java.lang.String> environment)

addEnvironmentItem

public MLModelFactors addEnvironmentItem(java.lang.String environmentItem)

getEnvironment

public java.util.List<java.lang.String> getEnvironment()

A further factor affecting MLModel performance is the environment in which it is deployed.

Returns:

environment

setEnvironment

public void setEnvironment(java.util.List<java.lang.String> environment)

equals

public boolean equals(java.lang.Object o)

Overrides:

equals in class java.lang.Object

hashCode

public int hashCode()

Overrides:

hashCode in class java.lang.Object

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object