#[non_exhaustive]pub struct CreateMlModelInput {
pub ml_model_id: Option<String>,
pub ml_model_name: Option<String>,
pub ml_model_type: Option<MlModelType>,
pub parameters: Option<HashMap<String, String>>,
pub training_data_source_id: Option<String>,
pub recipe: Option<String>,
pub recipe_uri: Option<String>,
}
Fields (Non-exhaustive)§
This struct is marked as non-exhaustive
Struct { .. }
syntax; cannot be matched against without a wildcard ..
; and struct update syntax will not work.ml_model_id: Option<String>
A user-supplied ID that uniquely identifies the MLModel
.
ml_model_name: Option<String>
A user-supplied name or description of the MLModel
.
ml_model_type: Option<MlModelType>
The category of supervised learning that this MLModel
will address. Choose from the following types:
-
Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. -
Choose
BINARY
if theMLModel
result has two possible values. -
Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
parameters: Option<HashMap<String, String>>
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
. 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 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. 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 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.
training_data_source_id: Option<String>
The DataSource
that points to the training data.
recipe: Option<String>
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_uri: Option<String>
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.
Implementations§
source§impl CreateMlModelInput
impl CreateMlModelInput
sourcepub fn ml_model_id(&self) -> Option<&str>
pub fn ml_model_id(&self) -> Option<&str>
A user-supplied ID that uniquely identifies the MLModel
.
sourcepub fn ml_model_name(&self) -> Option<&str>
pub fn ml_model_name(&self) -> Option<&str>
A user-supplied name or description of the MLModel
.
sourcepub fn ml_model_type(&self) -> Option<&MlModelType>
pub fn ml_model_type(&self) -> Option<&MlModelType>
The category of supervised learning that this MLModel
will address. Choose from the following types:
-
Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. -
Choose
BINARY
if theMLModel
result has two possible values. -
Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
sourcepub fn parameters(&self) -> Option<&HashMap<String, String>>
pub fn parameters(&self) -> Option<&HashMap<String, String>>
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
. 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 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. 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 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.
sourcepub fn training_data_source_id(&self) -> Option<&str>
pub fn training_data_source_id(&self) -> Option<&str>
The DataSource
that points to the training data.
sourcepub fn recipe(&self) -> Option<&str>
pub fn recipe(&self) -> Option<&str>
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.
sourcepub fn recipe_uri(&self) -> Option<&str>
pub fn recipe_uri(&self) -> Option<&str>
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.
source§impl CreateMlModelInput
impl CreateMlModelInput
sourcepub fn builder() -> CreateMlModelInputBuilder
pub fn builder() -> CreateMlModelInputBuilder
Creates a new builder-style object to manufacture CreateMlModelInput
.
Trait Implementations§
source§impl Clone for CreateMlModelInput
impl Clone for CreateMlModelInput
source§fn clone(&self) -> CreateMlModelInput
fn clone(&self) -> CreateMlModelInput
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read moresource§impl Debug for CreateMlModelInput
impl Debug for CreateMlModelInput
source§impl PartialEq for CreateMlModelInput
impl PartialEq for CreateMlModelInput
source§fn eq(&self, other: &CreateMlModelInput) -> bool
fn eq(&self, other: &CreateMlModelInput) -> bool
self
and other
values to be equal, and is used
by ==
.impl StructuralPartialEq for CreateMlModelInput
Auto Trait Implementations§
impl Freeze for CreateMlModelInput
impl RefUnwindSafe for CreateMlModelInput
impl Send for CreateMlModelInput
impl Sync for CreateMlModelInput
impl Unpin for CreateMlModelInput
impl UnwindSafe for CreateMlModelInput
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
source§impl<T> Instrument for T
impl<T> Instrument for T
source§fn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
source§fn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
source§impl<T> IntoEither for T
impl<T> IntoEither for T
source§fn into_either(self, into_left: bool) -> Either<Self, Self>
fn into_either(self, into_left: bool) -> Either<Self, Self>
self
into a Left
variant of Either<Self, Self>
if into_left
is true
.
Converts self
into a Right
variant of Either<Self, Self>
otherwise. Read moresource§fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
self
into a Left
variant of Either<Self, Self>
if into_left(&self)
returns true
.
Converts self
into a Right
variant of Either<Self, Self>
otherwise. Read more