SymetryML Documentation
Models

Select model (dataframe body)

Dataframe-body variant of select model: instead of a `{dsName}` path segment referencing an already-uploaded data source, the request body carries both the `dataframe` (raw data) and `mlcontext` (`inputAttributes`/`targets`) directly. Attribute resolution still goes through the project's own learned data, not the inline dataframe — the project must already have been `/learn`ed with the same schema; the body's `dataframe` supplies the scoring/validation data. Schedules an asynchronous job (same response shape/status strings as the `{dsName}` variant).

POST
/{user}/projects/{project}/selectModel

Dataframe-body variant of select model: instead of a {dsName} path segment referencing an already-uploaded data source, the request body carries both the dataframe (raw data) and mlcontext (inputAttributes/targets) directly. Attribute resolution still goes through the project's own learned data, not the inline dataframe — the project must already have been /learned with the same schema; the body's dataframe supplies the scoring/validation data. Schedules an asynchronous job (same response shape/status strings as the {dsName} variant).

Authorization

SymetryMLAuth
AuthorizationBearer <token>

HMAC-SHA256 signature-based authentication: requests are signed with your secret key and sent with the Customer-ID, Sym-date, Authorization, Content-MD5 and sym-version headers. See SymetryML REST API Security for the signature algorithm and a complete example.

In: header

Path Parameters

user*string

User/Customer identifier

project*string

Project identifier

Query Parameters

modelid*string

Unique identifier for the model.

algo*string

Machine learning algorithm to use.

Value in

  • "bayes"
  • "covest"
  • "ecod"
  • "elasticnet"
  • "evt"
  • "hmm"
  • "km"
  • "kmeans"
  • "lasso"
  • "lr"
  • "lda"
  • "lsvm"
  • "lsvr"
  • "mc"
  • "mlda"
  • "mlr"
  • "mqda"
  • "pcr"
  • "plsq"
  • "powerreg"
  • "ridge"
  • "rf_classifier"
  • "rf_regressor"
  • "rf_anomaly"
  • "oospca"
  • "hba"
  • "repca"

Request Body

application/json

TypeScript Definitions

Use the request body type in TypeScript.

Response Body

application/json

application/json

curl -X POST "https://example.com/c1/projects/nbra_prj_iris/selectModel?modelid=selectmodel_df_lda&algo=lda" \  -H "Content-Type: application/json" \  -d '{    "mlcontext": {      "inputAttributes": [        "0",        "1",        "2",        "3"      ],      "targets": [        "13"      ],      "extraParameters": {        "selector_type": "selector_type_simple"      }    },    "dataframe": {      "attributeNames": [        "sepal_length",        "sepal_width",        "petal_length",        "petal_width",        "Iris_versicolor"      ],      "attributeTypes": [        "C",        "C",        "C",        "C",        "B"      ],      "data": [        [          "4.3",          "3",          "1.1",          "0.1",          "0"        ],        [          "7.0",          "3.2",          "4.7",          "1.4",          "1"        ]      ]    }  }'
{  "statusCode": 200,  "statusString": "Job is finished",  "values": {}}
{  "statusCode": 202,  "statusString": "SelectModel Job Created",  "values": {}}

Select model POST

Select a given model type. This operation automatically chooses the best model: under the hood, SymetryML builds many different models across various permutations of the selected input attributes. The request body is an `MLContext` object.

Delete prediction results DELETE

Deletes a previously-saved input→output prediction pairing recorded against this model.