FeaturizationConfig class
In a CreatePredictor operation, the specified algorithm trains a model using the specified dataset group. You can optionally tell the operation to modify data fields prior to training a model. These modifications are referred to as featurization.
You define featurization using the FeaturizationConfig object.
You specify an array of transformations, one for each field that you want to
featurize. You then include the FeaturizationConfig object in
your CreatePredictor request. Amazon Forecast applies the
featurization to the TARGET_TIME_SERIES and
RELATED_TIME_SERIES datasets before model training.
You can create multiple featurization configurations. For example, you might
call the CreatePredictor operation twice by specifying
different featurization configurations.
Constructors
-
FeaturizationConfig({required String forecastFrequency, List<
Featurization> ? featurizations, List<String> ? forecastDimensions}) -
FeaturizationConfig.fromJson(Map<
String, dynamic> json) -
factory
Properties
-
featurizations
→ List<
Featurization> ? -
An array of featurization (transformation) information for the fields of a
dataset.
final
-
forecastDimensions
→ List<
String> ? -
An array of dimension (field) names that specify how to group the generated
forecast.
final
- forecastFrequency → String
-
The frequency of predictions in a forecast.
final
- hashCode → int
-
The hash code for this object.
no setterinherited
- runtimeType → Type
-
A representation of the runtime type of the object.
no setterinherited
Methods
-
noSuchMethod(
Invocation invocation) → dynamic -
Invoked when a nonexistent method or property is accessed.
inherited
-
toJson(
) → Map< String, dynamic> -
toString(
) → String -
A string representation of this object.
inherited
Operators
-
operator ==(
Object other) → bool -
The equality operator.
inherited