Model Predict

The model_predict session, is an implementation that performs a prediction, using a previously generated model, from a model_generate session. The required attributes, for the corresponding session, is sent to the /load-data endpoint, which is demonstrated as follows:

• svm example
• svr example

Note: the content of each of the above examples, can be substituted for the data attribute, in a given POST request:

import requests

endpoint = 'https://192.168.99.101:9090/load-data'
headers = {
    'Authorization': 'Bearer ' + token,
    'Content-Type': 'application/json'
}

requests.post(endpoint, headers=headers, data=json_string_here)

Note: more information, regarding how to obtain a valid token, can be further reviewed, in the /login documentation.

The following properties define the above data attribute:

• collection: collection of dataset documents, used to generate a model, via the model_generate session, which is also used to name the corresponding model
• session_type: corresponds to one of the following session types:
  • data_new
  • data_append
  • model_generate
  • model_predict
• model_id: corresponds to the id associated with a previous model_generate session.
• prediction_input[]: an array of prediction input, supplied to the previously generated model to compute a prediction.