Reference documentation and code samples for the Cloud AutoML V1beta1 API class Google::Cloud::AutoML::V1beta1::PredictRequest.
Request message for PredictionService.Predict.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#name
def name() -> ::String
- (::String) — Required. Name of the model requested to serve the prediction.
#name=
def name=(value) -> ::String
- value (::String) — Required. Name of the model requested to serve the prediction.
- (::String) — Required. Name of the model requested to serve the prediction.
#params
def params() -> ::Google::Protobuf::Map{::String => ::String}
-
(::Google::Protobuf::Map{::String => ::String}) —
Additional domain-specific parameters, any string must be up to 25000 characters long.
- For Image Classification:
score_threshold
- (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5.- For Image Object Detection:
score_threshold
- (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5.max_bounding_box_count
- (int64) No more than this number of bounding boxes will be returned in the response. Default is 100, the requested value may be limited by server.- For Tables: feature_importance - (boolean) Whether feature importance should be populated in the returned TablesAnnotation. The default is false.
#params=
def params=(value) -> ::Google::Protobuf::Map{::String => ::String}
-
value (::Google::Protobuf::Map{::String => ::String}) —
Additional domain-specific parameters, any string must be up to 25000 characters long.
- For Image Classification:
score_threshold
- (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5.- For Image Object Detection:
score_threshold
- (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5.max_bounding_box_count
- (int64) No more than this number of bounding boxes will be returned in the response. Default is 100, the requested value may be limited by server.- For Tables: feature_importance - (boolean) Whether feature importance should be populated in the returned TablesAnnotation. The default is false.
-
(::Google::Protobuf::Map{::String => ::String}) —
Additional domain-specific parameters, any string must be up to 25000 characters long.
- For Image Classification:
score_threshold
- (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5.- For Image Object Detection:
score_threshold
- (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5.max_bounding_box_count
- (int64) No more than this number of bounding boxes will be returned in the response. Default is 100, the requested value may be limited by server.- For Tables: feature_importance - (boolean) Whether feature importance should be populated in the returned TablesAnnotation. The default is false.
#payload
def payload() -> ::Google::Cloud::AutoML::V1beta1::ExamplePayload
- (::Google::Cloud::AutoML::V1beta1::ExamplePayload) — Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.
#payload=
def payload=(value) -> ::Google::Cloud::AutoML::V1beta1::ExamplePayload
- value (::Google::Cloud::AutoML::V1beta1::ExamplePayload) — Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.
- (::Google::Cloud::AutoML::V1beta1::ExamplePayload) — Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.