...
Name | Affiliation |
|---|---|
| Cupid Chan | Pistevo Decision |
| Xiangxiang Meng | Redfin |
| Deepak Karuppiah | MicroStrategy |
| Nancy Rausch | SAS |
| Dalton Ruer | Qlik |
| Sachin Sinha | Microsoft |
| Yi Shao | IBM |
| Jeffrey Tang | Predibaes |
| Lingyan Yin | Salesforce |
Get Model Metrics
Get core evaluation metrics for a trained model.
...
function GetModelMetrics(UUID) -> Metrics
Example response:
{ |
Predict Using Trained Model
function PredictWithModel(UUID, dataConfig) -> Predictions |
Example params
...
{
"uuid": "abcdef12345",
"data":{
"sourceType":"snowflake",
"endpoint":"some/endpoint",
"bearerToken":"...",
"query":"SELECT foo FROM bar WHERE baz"
}
}
A very similar data stanza to the train request, designating the feature data on which to predict.
Example response (as JSON here for convenience, not necessarily for large responses):
...
{
"data":[
{
"customerAge":2,
"activeInLastMonth":"false",
"predicted__canceledSubscription":"true"
},
{
"customerAge":9,
"activeInLastMonth":"true",
"predicted__canceledSubscription":"false"
}
]
}
Note that directly returning a large response set is not a good idea. In practice, the results could be streamed through something like a persistent socket connection.