Struct aws_sdk_machinelearning::model::ml_model::Builder [−][src]
#[non_exhaustive]pub struct Builder { /* fields omitted */ }
Expand description
A builder for MlModel
Implementations
The ID assigned to the MLModel
at creation.
The ID assigned to the MLModel
at creation.
The ID of the training DataSource
. The CreateMLModel
operation uses the TrainingDataSourceId
.
The ID of the training DataSource
. The CreateMLModel
operation uses the TrainingDataSourceId
.
The AWS user account from which the MLModel
was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
The AWS user account from which the MLModel
was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
The time that the MLModel
was created. The time is expressed in epoch time.
The time that the MLModel
was created. The time is expressed in epoch time.
The time of the most recent edit to the MLModel
. The time is expressed in epoch time.
The time of the most recent edit to the MLModel
. The time is expressed in epoch time.
A user-supplied name or description of the MLModel
.
A user-supplied name or description of the MLModel
.
The current status of an MLModel
. This element can have one of the following values:
-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to create anMLModel
. -
INPROGRESS
- The creation process is underway. -
FAILED
- The request to create anMLModel
didn't run to completion. The model isn't usable. -
COMPLETED
- The creation process completed successfully. -
DELETED
- TheMLModel
is marked as deleted. It isn't usable.
The current status of an MLModel
. This element can have one of the following values:
-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to create anMLModel
. -
INPROGRESS
- The creation process is underway. -
FAILED
- The request to create anMLModel
didn't run to completion. The model isn't usable. -
COMPLETED
- The creation process completed successfully. -
DELETED
- TheMLModel
is marked as deleted. It isn't usable.
Long integer type that is a 64-bit signed number.
Long integer type that is a 64-bit signed number.
The current endpoint of the MLModel
.
The current endpoint of the MLModel
.
Adds a key-value pair to training_parameters
.
To override the contents of this collection use set_training_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
to2147483648
. The default value is33554432
. -
sgd.maxPasses
- The number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from1
to10000
. The default value is10
. -
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 areauto
andnone
. The default value isnone
. -
sgd.l1RegularizationAmount
- The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as1.0E-08
.The value is a double that ranges from
0
toMAX_DOUBLE
. The default is to not use L1 normalization. This parameter can't be used whenL2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- The coefficient regularization L2 norm, which 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 as1.0E-08
.The value is a double that ranges from
0
toMAX_DOUBLE
. The default is to not use L2 normalization. This parameter can't be used whenL1
is specified. Use this parameter sparingly.
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
to2147483648
. The default value is33554432
. -
sgd.maxPasses
- The number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from1
to10000
. The default value is10
. -
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 areauto
andnone
. The default value isnone
. -
sgd.l1RegularizationAmount
- The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as1.0E-08
.The value is a double that ranges from
0
toMAX_DOUBLE
. The default is to not use L1 normalization. This parameter can't be used whenL2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- The coefficient regularization L2 norm, which 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 as1.0E-08
.The value is a double that ranges from
0
toMAX_DOUBLE
. The default is to not use L2 normalization. This parameter can't be used whenL1
is specified. Use this parameter sparingly.
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
The algorithm used to train the MLModel
. The following algorithm is supported:
-
SGD
-- Stochastic gradient descent. The goal ofSGD
is to minimize the gradient of the loss function.
The algorithm used to train the MLModel
. The following algorithm is supported:
-
SGD
-- Stochastic gradient descent. The goal ofSGD
is to minimize the gradient of the loss function.
Identifies the MLModel
category. The following are the available types:
-
REGRESSION
- Produces a numeric result. For example, "What price should a house be listed at?" -
BINARY
- Produces one of two possible results. For example, "Is this a child-friendly web site?". -
MULTICLASS
- Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
Identifies the MLModel
category. The following are the available types:
-
REGRESSION
- Produces a numeric result. For example, "What price should a house be listed at?" -
BINARY
- Produces one of two possible results. For example, "Is this a child-friendly web site?". -
MULTICLASS
- Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
The time of the most recent edit to the ScoreThreshold
. The time is expressed in epoch time.
The time of the most recent edit to the ScoreThreshold
. The time is expressed in epoch time.
A description of the most recent details about accessing the MLModel
.
A description of the most recent details about accessing the MLModel
.
Long integer type that is a 64-bit signed number.
Long integer type that is a 64-bit signed number.
A timestamp represented in epoch time.
A timestamp represented in epoch time.
A timestamp represented in epoch time.
A timestamp represented in epoch time.
Trait Implementations
Auto Trait Implementations
impl RefUnwindSafe for Builder
impl UnwindSafe for Builder
Blanket Implementations
Mutably borrows from an owned value. Read more
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more