SymetryML Documentation
Models

Auto-select optimal model (dataframe body)

Dataframe-body variant of auto-select: 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, including the shared `"SelectModel Job Created"` status string).

POST
/{user}/projects/{project}/autoSelect

Dataframe-body variant of auto-select: 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, including the shared "SelectModel Job Created" status string).

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.

task*string

Machine learning task.

Value in

  • "binary_classifier"
  • "multiclass_classifier"
  • "regression"
  • "anomaly"

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/autoSelect?modelid=autoselect_df_binary&task=binary_classifier" \  -H "Content-Type: application/json" \  -d '{    "mlcontext": {      "inputAttributes": [        "0",        "1",        "2",        "3"      ],      "targets": [        "13"      ],      "extraParameters": {}    },    "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": {}}

Auto-select optimal model POST

Auto-select the optimal model for a given task (binary classifier or regression). Under the hood, SymetryML builds many different models across various permutations of the selected input attributes, and also considers various model types for the given task, then chooses the best one. The request body is an `MLContext` object.

Build machine learning model POST

Build various types of machine learning models, including LDA, LSVM, LSVR, MLR, and third-party models. Supports attribute range specifications, SVD reduction, and various model types. The request body is an `MLContext` object. `ghmm` and `chmm` additionally require a `sequence`-type project, and pass the attribute name as a literal string inside `inputAttributes`/`targets` rather than `inputAttributeNames`/`targetAttributeNames`.