MachineLearning / Client / predict
predict#
- MachineLearning.Client.predict(**kwargs)#
Generates a prediction for the observation using the specified
ML Model
.Note: Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
See also: AWS API Documentation
Request Syntax
response = client.predict( MLModelId='string', Record={ 'string': 'string' }, PredictEndpoint='string' )
- Parameters:
MLModelId (string) –
[REQUIRED]
A unique identifier of the
MLModel
.Record (dict) –
[REQUIRED]
A map of variable name-value pairs that represent an observation.
(string) –
The name of a variable. Currently it’s used to specify the name of the target value, label, weight, and tags.
(string) –
The value of a variable. Currently it’s used to specify values of the target value, weights, and tag variables and for filtering variable values.
PredictEndpoint (string) – [REQUIRED]
- Return type:
dict
- Returns:
Response Syntax
{ 'Prediction': { 'predictedLabel': 'string', 'predictedValue': ..., 'predictedScores': { 'string': ... }, 'details': { 'string': 'string' } } }
Response Structure
(dict) –
Prediction (dict) –
The output from a
Predict
operation:Details
- Contains the following attributes:DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS
DetailsAttributes.ALGORITHM - SGD
PredictedLabel
- Present for either aBINARY
orMULTICLASS
MLModel
request.PredictedScores
- Contains the raw classification score corresponding to each label.PredictedValue
- Present for aREGRESSION
MLModel
request.
predictedLabel (string) –
The prediction label for either a
BINARY
orMULTICLASS
MLModel
.predictedValue (float) –
The prediction value for
REGRESSION
MLModel
.predictedScores (dict) –
Provides the raw classification score corresponding to each label.
(string) –
(float) –
details (dict) –
Provides any additional details regarding the prediction.
(string) –
Contains the key values of
DetailsMap
:PredictiveModelType
- Indicates the type of theMLModel
.Algorithm
- Indicates the algorithm that was used for theMLModel
.
(string) –
Exceptions
MachineLearning.Client.exceptions.InvalidInputException
MachineLearning.Client.exceptions.ResourceNotFoundException
MachineLearning.Client.exceptions.LimitExceededException
MachineLearning.Client.exceptions.InternalServerException
MachineLearning.Client.exceptions.PredictorNotMountedException