Creates a new MLModel using the DataSource and the recipe as information sources.
See https://www.paws-r-sdk.com/docs/machinelearning_create_ml_model/ for full documentation.
machinelearning_create_ml_model(
MLModelId,
MLModelName = NULL,
MLModelType,
Parameters = NULL,
TrainingDataSourceId,
Recipe = NULL,
RecipeUri = NULL
)[required] A user-supplied ID that uniquely identifies the MLModel.
A user-supplied name or description of the MLModel.
[required] 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.
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.
[required] The DataSource that points to the training data.
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.
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.