@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class CreateMLModelRequest extends AmazonWebServiceRequest implements Serializable, Cloneable
NOOP
Constructor and Description |
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CreateMLModelRequest() |
Modifier and Type | Method and Description |
---|---|
CreateMLModelRequest |
addParametersEntry(String key,
String value)
Add a single Parameters entry
|
CreateMLModelRequest |
clearParametersEntries()
Removes all the entries added into Parameters.
|
CreateMLModelRequest |
clone()
Creates a shallow clone of this object for all fields except the handler context.
|
boolean |
equals(Object obj) |
String |
getMLModelId()
A user-supplied ID that uniquely identifies the
MLModel . |
String |
getMLModelName()
A user-supplied name or description of the
MLModel . |
String |
getMLModelType()
The category of supervised learning that this
MLModel will address. |
Map<String,String> |
getParameters()
A list of the training parameters in the
MLModel . |
String |
getRecipe()
The data recipe for creating the
MLModel . |
String |
getRecipeUri()
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the
MLModel
recipe. |
String |
getTrainingDataSourceId()
The
DataSource that points to the training data. |
int |
hashCode() |
void |
setMLModelId(String mLModelId)
A user-supplied ID that uniquely identifies the
MLModel . |
void |
setMLModelName(String mLModelName)
A user-supplied name or description of the
MLModel . |
void |
setMLModelType(MLModelType mLModelType)
The category of supervised learning that this
MLModel will address. |
void |
setMLModelType(String mLModelType)
The category of supervised learning that this
MLModel will address. |
void |
setParameters(Map<String,String> parameters)
A list of the training parameters in the
MLModel . |
void |
setRecipe(String recipe)
The data recipe for creating the
MLModel . |
void |
setRecipeUri(String recipeUri)
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the
MLModel
recipe. |
void |
setTrainingDataSourceId(String trainingDataSourceId)
The
DataSource that points to the training data. |
String |
toString()
Returns a string representation of this object.
|
CreateMLModelRequest |
withMLModelId(String mLModelId)
A user-supplied ID that uniquely identifies the
MLModel . |
CreateMLModelRequest |
withMLModelName(String mLModelName)
A user-supplied name or description of the
MLModel . |
CreateMLModelRequest |
withMLModelType(MLModelType mLModelType)
The category of supervised learning that this
MLModel will address. |
CreateMLModelRequest |
withMLModelType(String mLModelType)
The category of supervised learning that this
MLModel will address. |
CreateMLModelRequest |
withParameters(Map<String,String> parameters)
A list of the training parameters in the
MLModel . |
CreateMLModelRequest |
withRecipe(String recipe)
The data recipe for creating the
MLModel . |
CreateMLModelRequest |
withRecipeUri(String recipeUri)
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the
MLModel
recipe. |
CreateMLModelRequest |
withTrainingDataSourceId(String trainingDataSourceId)
The
DataSource that points to the training data. |
addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeout
public void setMLModelId(String mLModelId)
A user-supplied ID that uniquely identifies the MLModel
.
mLModelId
- A user-supplied ID that uniquely identifies the MLModel
.public String getMLModelId()
A user-supplied ID that uniquely identifies the MLModel
.
MLModel
.public CreateMLModelRequest withMLModelId(String mLModelId)
A user-supplied ID that uniquely identifies the MLModel
.
mLModelId
- A user-supplied ID that uniquely identifies the MLModel
.public void setMLModelName(String mLModelName)
A user-supplied name or description of the MLModel
.
mLModelName
- A user-supplied name or description of the MLModel
.public String getMLModelName()
A user-supplied name or description of the MLModel
.
MLModel
.public CreateMLModelRequest withMLModelName(String mLModelName)
A user-supplied name or description of the MLModel
.
mLModelName
- A user-supplied name or description of the MLModel
.public void setMLModelType(String mLModelType)
The category of supervised learning that this MLModel
will address. Choose from the following types:
Choose REGRESSION
if the MLModel
will be used to predict a numeric value.
Choose BINARY
if the MLModel
result has two possible values.
Choose MULTICLASS
if the MLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
mLModelType
- The category of supervised learning that this MLModel
will address. Choose from the following
types:
Choose REGRESSION
if the MLModel
will be used to predict a numeric value.
Choose BINARY
if the MLModel
result has two possible values.
Choose MULTICLASS
if the MLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
MLModelType
public String getMLModelType()
The category of supervised learning that this MLModel
will address. Choose from the following types:
Choose REGRESSION
if the MLModel
will be used to predict a numeric value.
Choose BINARY
if the MLModel
result has two possible values.
Choose MULTICLASS
if the MLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
MLModel
will address. Choose from the
following types:
Choose REGRESSION
if the MLModel
will be used to predict a numeric value.
Choose BINARY
if the MLModel
result has two possible values.
Choose MULTICLASS
if the MLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
MLModelType
public CreateMLModelRequest withMLModelType(String mLModelType)
The category of supervised learning that this MLModel
will address. Choose from the following types:
Choose REGRESSION
if the MLModel
will be used to predict a numeric value.
Choose BINARY
if the MLModel
result has two possible values.
Choose MULTICLASS
if the MLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
mLModelType
- The category of supervised learning that this MLModel
will address. Choose from the following
types:
Choose REGRESSION
if the MLModel
will be used to predict a numeric value.
Choose BINARY
if the MLModel
result has two possible values.
Choose MULTICLASS
if the MLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
MLModelType
public void setMLModelType(MLModelType mLModelType)
The category of supervised learning that this MLModel
will address. Choose from the following types:
Choose REGRESSION
if the MLModel
will be used to predict a numeric value.
Choose BINARY
if the MLModel
result has two possible values.
Choose MULTICLASS
if the MLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
mLModelType
- The category of supervised learning that this MLModel
will address. Choose from the following
types:
Choose REGRESSION
if the MLModel
will be used to predict a numeric value.
Choose BINARY
if the MLModel
result has two possible values.
Choose MULTICLASS
if the MLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
MLModelType
public CreateMLModelRequest withMLModelType(MLModelType mLModelType)
The category of supervised learning that this MLModel
will address. Choose from the following types:
Choose REGRESSION
if the MLModel
will be used to predict a numeric value.
Choose BINARY
if the MLModel
result has two possible values.
Choose MULTICLASS
if the MLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
mLModelType
- The category of supervised learning that this MLModel
will address. Choose from the following
types:
Choose REGRESSION
if the MLModel
will be used to predict a numeric value.
Choose BINARY
if the MLModel
result has two possible values.
Choose MULTICLASS
if the MLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
MLModelType
public Map<String,String> getParameters()
A list of the training parameters in the MLModel
. The list is implemented as a map of key-value
pairs.
The following is the current set of training parameters:
sgd.maxMLModelSizeInBytes
- The maximum allowed size of the model. Depending on the input data, the
size of the model might affect its performance.
The value is an integer that ranges from 100000
to 2147483648
. The default value is
33554432
.
sgd.maxPasses
- The number of times that the training process traverses the observations to build
the MLModel
. The value is an integer that ranges from 1
to 10000
. The
default value is 10
.
sgd.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling the data improves a
model's ability to find the optimal solution for a variety of data types. The valid values are auto
and none
. The default value is none
. We strongly recommend that you shuffle your data.
sgd.l1RegularizationAmount
- The coefficient regularization L1 norm. It controls overfitting the
data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature
set. If you use this parameter, start by specifying a small value, such as 1.0E-08
.
The value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not use L1
normalization. This parameter can't be used when L2
is specified. Use this parameter sparingly.
sgd.l2RegularizationAmount
- The coefficient regularization L2 norm. It controls overfitting the
data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this
parameter, start by specifying a small value, such as 1.0E-08
.
The value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not use L2
normalization. This parameter can't be used when L1
is specified. Use this parameter sparingly.
MLModel
. The list is implemented as a map of
key-value pairs.
The following is the current set of training parameters:
sgd.maxMLModelSizeInBytes
- The maximum allowed size of the model. Depending on the input
data, the size of the model might affect its performance.
The value is an integer that ranges from 100000
to 2147483648
. The default
value is 33554432
.
sgd.maxPasses
- The number of times that the training process traverses the observations to
build the MLModel
. The value is an integer that ranges from 1
to
10000
. The default value is 10
.
sgd.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling the data improves
a model's ability to find the optimal solution for a variety of data types. The valid values are
auto
and none
. The default value is none
. We strongly recommend
that you shuffle your data.
sgd.l1RegularizationAmount
- The coefficient regularization L1 norm. It controls overfitting
the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a
sparse feature set. If you use this parameter, start by specifying a small value, such as
1.0E-08
.
The value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not
use L1 normalization. This parameter can't be used when L2
is specified. Use this parameter
sparingly.
sgd.l2RegularizationAmount
- The coefficient regularization L2 norm. It controls overfitting
the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If
you use this parameter, start by specifying a small value, such as 1.0E-08
.
The value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not
use L2 normalization. This parameter can't be used when L1
is specified. Use this parameter
sparingly.
public void setParameters(Map<String,String> parameters)
A list of the training parameters in the MLModel
. The list is implemented as a map of key-value
pairs.
The following is the current set of training parameters:
sgd.maxMLModelSizeInBytes
- The maximum allowed size of the model. Depending on the input data, the
size of the model might affect its performance.
The value is an integer that ranges from 100000
to 2147483648
. The default value is
33554432
.
sgd.maxPasses
- The number of times that the training process traverses the observations to build
the MLModel
. The value is an integer that ranges from 1
to 10000
. The
default value is 10
.
sgd.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling the data improves a
model's ability to find the optimal solution for a variety of data types. The valid values are auto
and none
. The default value is none
. We strongly recommend that you shuffle your data.
sgd.l1RegularizationAmount
- The coefficient regularization L1 norm. It controls overfitting the
data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature
set. If you use this parameter, start by specifying a small value, such as 1.0E-08
.
The value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not use L1
normalization. This parameter can't be used when L2
is specified. Use this parameter sparingly.
sgd.l2RegularizationAmount
- The coefficient regularization L2 norm. It controls overfitting the
data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this
parameter, start by specifying a small value, such as 1.0E-08
.
The value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not use L2
normalization. This parameter can't be used when L1
is specified. Use this parameter sparingly.
parameters
- A list of the training parameters in the MLModel
. The list is implemented as a map of
key-value pairs.
The following is the current set of training parameters:
sgd.maxMLModelSizeInBytes
- The maximum allowed size of the model. Depending on the input
data, the size of the model might affect its performance.
The value is an integer that ranges from 100000
to 2147483648
. The default value
is 33554432
.
sgd.maxPasses
- The number of times that the training process traverses the observations to
build the MLModel
. The value is an integer that ranges from 1
to
10000
. The default value is 10
.
sgd.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling the data improves a
model's ability to find the optimal solution for a variety of data types. The valid values are
auto
and none
. The default value is none
. We strongly recommend
that you shuffle your data.
sgd.l1RegularizationAmount
- The coefficient regularization L1 norm. It controls overfitting
the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse
feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08
.
The value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not
use L1 normalization. This parameter can't be used when L2
is specified. Use this parameter
sparingly.
sgd.l2RegularizationAmount
- The coefficient regularization L2 norm. It controls overfitting
the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If
you use this parameter, start by specifying a small value, such as 1.0E-08
.
The value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not
use L2 normalization. This parameter can't be used when L1
is specified. Use this parameter
sparingly.
public CreateMLModelRequest withParameters(Map<String,String> parameters)
A list of the training parameters in the MLModel
. The list is implemented as a map of key-value
pairs.
The following is the current set of training parameters:
sgd.maxMLModelSizeInBytes
- The maximum allowed size of the model. Depending on the input data, the
size of the model might affect its performance.
The value is an integer that ranges from 100000
to 2147483648
. The default value is
33554432
.
sgd.maxPasses
- The number of times that the training process traverses the observations to build
the MLModel
. The value is an integer that ranges from 1
to 10000
. The
default value is 10
.
sgd.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling the data improves a
model's ability to find the optimal solution for a variety of data types. The valid values are auto
and none
. The default value is none
. We strongly recommend that you shuffle your data.
sgd.l1RegularizationAmount
- The coefficient regularization L1 norm. It controls overfitting the
data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature
set. If you use this parameter, start by specifying a small value, such as 1.0E-08
.
The value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not use L1
normalization. This parameter can't be used when L2
is specified. Use this parameter sparingly.
sgd.l2RegularizationAmount
- The coefficient regularization L2 norm. It controls overfitting the
data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this
parameter, start by specifying a small value, such as 1.0E-08
.
The value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not use L2
normalization. This parameter can't be used when L1
is specified. Use this parameter sparingly.
parameters
- A list of the training parameters in the MLModel
. The list is implemented as a map of
key-value pairs.
The following is the current set of training parameters:
sgd.maxMLModelSizeInBytes
- The maximum allowed size of the model. Depending on the input
data, the size of the model might affect its performance.
The value is an integer that ranges from 100000
to 2147483648
. The default value
is 33554432
.
sgd.maxPasses
- The number of times that the training process traverses the observations to
build the MLModel
. The value is an integer that ranges from 1
to
10000
. The default value is 10
.
sgd.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling the data improves a
model's ability to find the optimal solution for a variety of data types. The valid values are
auto
and none
. The default value is none
. We strongly recommend
that you shuffle your data.
sgd.l1RegularizationAmount
- The coefficient regularization L1 norm. It controls overfitting
the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse
feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08
.
The value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not
use L1 normalization. This parameter can't be used when L2
is specified. Use this parameter
sparingly.
sgd.l2RegularizationAmount
- The coefficient regularization L2 norm. It controls overfitting
the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If
you use this parameter, start by specifying a small value, such as 1.0E-08
.
The value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not
use L2 normalization. This parameter can't be used when L1
is specified. Use this parameter
sparingly.
public CreateMLModelRequest addParametersEntry(String key, String value)
public CreateMLModelRequest clearParametersEntries()
public void setTrainingDataSourceId(String trainingDataSourceId)
The DataSource
that points to the training data.
trainingDataSourceId
- The DataSource
that points to the training data.public String getTrainingDataSourceId()
The DataSource
that points to the training data.
DataSource
that points to the training data.public CreateMLModelRequest withTrainingDataSourceId(String trainingDataSourceId)
The DataSource
that points to the training data.
trainingDataSourceId
- The DataSource
that points to the training data.public void setRecipe(String recipe)
The data recipe for creating the MLModel
. You must specify either the recipe or its URI. If you
don't specify a recipe or its URI, Amazon ML creates a default.
recipe
- The data recipe for creating the MLModel
. You must specify either the recipe or its URI. If
you don't specify a recipe or its URI, Amazon ML creates a default.public String getRecipe()
The data recipe for creating the MLModel
. You must specify either the recipe or its URI. If you
don't specify a recipe or its URI, Amazon ML creates a default.
MLModel
. You must specify either the recipe or its URI. If
you don't specify a recipe or its URI, Amazon ML creates a default.public CreateMLModelRequest withRecipe(String recipe)
The data recipe for creating the MLModel
. You must specify either the recipe or its URI. If you
don't specify a recipe or its URI, Amazon ML creates a default.
recipe
- The data recipe for creating the MLModel
. You must specify either the recipe or its URI. If
you don't specify a recipe or its URI, Amazon ML creates a default.public void setRecipeUri(String recipeUri)
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel
recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML
creates a default.
recipeUri
- The Amazon Simple Storage Service (Amazon S3) location and file name that contains the
MLModel
recipe. You must specify either the recipe or its URI. If you don't specify a recipe
or its URI, Amazon ML creates a default.public String getRecipeUri()
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel
recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML
creates a default.
MLModel
recipe. You must specify either the recipe or its URI. If you don't specify a recipe
or its URI, Amazon ML creates a default.public CreateMLModelRequest withRecipeUri(String recipeUri)
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel
recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML
creates a default.
recipeUri
- The Amazon Simple Storage Service (Amazon S3) location and file name that contains the
MLModel
recipe. You must specify either the recipe or its URI. If you don't specify a recipe
or its URI, Amazon ML creates a default.public String toString()
toString
in class Object
Object.toString()
public CreateMLModelRequest clone()
AmazonWebServiceRequest
clone
in class AmazonWebServiceRequest
Object.clone()