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Class: Aws::MachineLearning::Types::MLModel
- Inherits:
-
Struct
- Object
- Struct
- Aws::MachineLearning::Types::MLModel
- Defined in:
- (unknown)
Overview
Represents the output of a GetMLModel
operation.
The content consists of the detailed metadata and the current status of the MLModel
.
Instance Attribute Summary collapse
-
#algorithm ⇒ String
The algorithm used to train the
MLModel
. -
#compute_time ⇒ Integer
Long integer type that is a 64-bit signed number.
.
-
#created_at ⇒ Time
The time that the
MLModel
was created. -
#created_by_iam_user ⇒ String
The AWS user account from which the
MLModel
was created. -
#endpoint_info ⇒ Types::RealtimeEndpointInfo
The current endpoint of the
MLModel
. -
#finished_at ⇒ Time
A timestamp represented in epoch time.
.
-
#input_data_location_s3 ⇒ String
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
-
#last_updated_at ⇒ Time
The time of the most recent edit to the
MLModel
. -
#message ⇒ String
A description of the most recent details about accessing the
MLModel
. -
#ml_model_id ⇒ String
The ID assigned to the
MLModel
at creation. -
#ml_model_type ⇒ String
Identifies the
MLModel
category. -
#name ⇒ String
A user-supplied name or description of the
MLModel
. -
#score_threshold ⇒ Float
-
#score_threshold_last_updated_at ⇒ Time
The time of the most recent edit to the
ScoreThreshold
. -
#size_in_bytes ⇒ Integer
Long integer type that is a 64-bit signed number.
.
-
#started_at ⇒ Time
A timestamp represented in epoch time.
.
-
#status ⇒ String
The current status of an
MLModel
. -
#training_data_source_id ⇒ String
The ID of the training
DataSource
. -
#training_parameters ⇒ Hash<String,String>
A list of the training parameters in the
MLModel
.
Instance Attribute Details
#algorithm ⇒ String
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.Possible values:
- sgd
#compute_time ⇒ Integer
Long integer type that is a 64-bit signed number.
#created_at ⇒ Time
The time that the MLModel
was created. The time is expressed in epoch
time.
#created_by_iam_user ⇒ String
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.
#endpoint_info ⇒ Types::RealtimeEndpointInfo
The current endpoint of the MLModel
.
#finished_at ⇒ Time
A timestamp represented in epoch time.
#input_data_location_s3 ⇒ String
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
#last_updated_at ⇒ Time
The time of the most recent edit to the MLModel
. The time is expressed
in epoch time.
#message ⇒ String
A description of the most recent details about accessing the MLModel
.
#ml_model_id ⇒ String
The ID assigned to the MLModel
at creation.
#ml_model_type ⇒ String
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<?oxy_delete author=\"annbech\" timestamp=\"20160328T175050-0700\" content=\" \"><?oxy_insert_start author=\"annbech\" timestamp=\"20160328T175050-0700\">-<?oxy_insert_end>risk trade?\".Possible values:
- REGRESSION
- BINARY
- MULTICLASS
#name ⇒ String
A user-supplied name or description of the MLModel
.
#score_threshold ⇒ Float
#score_threshold_last_updated_at ⇒ Time
The time of the most recent edit to the ScoreThreshold
. The time is
expressed in epoch time.
#size_in_bytes ⇒ Integer
Long integer type that is a 64-bit signed number.
#started_at ⇒ Time
A timestamp represented in epoch time.
#status ⇒ String
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.Possible values:
- PENDING
- INPROGRESS
- FAILED
- COMPLETED
- DELETED
#training_data_source_id ⇒ String
The ID of the training DataSource
. The CreateMLModel
operation uses
the TrainingDataSourceId
.
#training_parameters ⇒ Hash<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
.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.