How to predict, in details.
| Name | Type | Description | Notes |
|---|---|---|---|
| project_id | int | The destination project, of which objects will be predicted. | |
| source_project_ids | List[int] | The source projects, objects in them will serve as reference. | |
| learning_limit | int | When set (to a positive value), there will be this number of objects, per category, in the learning set. | [optional] |
| features | List[str] | The object features AKA free column, to use in the algorithm. Features must be common to all projects, source ones and destination one. | |
| categories | List[int] | In source projects, only objects validated with these categories will be considered. | |
| use_scn | bool | Use extra features, generated using the image, for improving the prediction. | [optional] [default to False] |
| pre_mapping | Dict[str, int] | Categories in keys become value one before launching the ML algorithm. Any unknown value is ignored. |
from ecotaxa_py_client.models.prediction_req import PredictionReq
# TODO update the JSON string below
json = "{}"
# create an instance of PredictionReq from a JSON string
prediction_req_instance = PredictionReq.from_json(json)
# print the JSON string representation of the object
print(PredictionReq.to_json())
# convert the object into a dict
prediction_req_dict = prediction_req_instance.to_dict()
# create an instance of PredictionReq from a dict
prediction_req_form_dict = prediction_req.from_dict(prediction_req_dict)