SymetryML Documentation
Models

Select model

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.

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

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.

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

dsName*string

Data source name

Query Parameters

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"
modelid*string

Unique identifier for the model

Request Body

application/json

TypeScript Definitions

Use the request body type in TypeScript.

Response Body

application/json

application/json

application/json

application/json

curl -X POST "https://example.com/c1/projects/nbra_prj_iris/selectModel/nbra_ds_sftp_iris?algo=lda&modelid=nbra_prj_iris_build_1" \  -H "Content-Type: application/json" \  -d '{    "inputAttributes": [      0,      1,      2,      3,      4,      5,      6,      7,      8,      9,      10,      11    ],    "targets": [      12    ],    "extraParameters": {      "matrix_use_pseudoinv": "false",      "sml_rcond_use": "false",      "sml_rcond_tolerance": "0.00000000000001",      "selector_type": "selector_type_fw_bw",      "selector_max_iterations": "5",      "sml_model_assessment_type": "auc"    }  }'
{  "statusCode": 200,  "statusString": "Job is finished",  "values": {}}
{  "statusCode": 202,  "statusString": "SelectModel Job Created",  "values": {}}
{  "statusCode": 400,  "statusString": "java.lang.IllegalArgumentException: Invalid Model Name: string is null",  "values": {}}
{  "statusCode": 500,  "statusString": "",  "values": {}}

Reduce model complexity POST

Schedules an asynchronous job that builds a **new** model via heuristic attribute-selection search over the given input attributes/target — the same kind of search `SELECT`/`AUTOSELECT` perform, not a size-reduction of a pre-existing model. Despite the name, no existing model is read or referenced anywhere in this request; `modelid` names the model this call creates. Note the path has **no `{model}` segment**. The request body is an `MLContext` object.

Select model (dataframe body) POST

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).