SymetryML Documentation
Models

Build machine learning model

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`.

POST
/{user}/projects/{project}/build

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.

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

algo*string

Machine learning algorithm to use. Must match the request body example selected below.

Value in

  • "bayes"
  • "chmm"
  • "copod"
  • "covest"
  • "ecod"
  • "elasticnet"
  • "evt"
  • "ghmm"
  • "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

svdreduce?boolean

Apply SVD reduction before building model

Defaultfalse

Request Body

application/json

TypeScript Definitions

Use the request body type in TypeScript.

Response Body

application/json

application/json

application/json

curl -X POST "https://example.com/c1/projects/nbra_prj_iris/build?algo=lda&modelid=nbra_prj_iris_build_1&svdreduce=false" \  -H "Content-Type: application/json" \  -d '{    "inputAttributes": [      0,      1,      2,      3    ],    "targets": [      12    ],    "extraParameters": {      "matrix_use_pseudoinv": "false",      "sml_rcond_use": "false",      "sml_rcond_tolerance": "0.00000000000001"    }  }'
{  "statusCode": 200,  "statusString": "Job is finished",  "values": {}}
{  "statusCode": 202,  "statusString": "Job Created",  "values": {}}

{  "statusCode": 400,  "statusString": "Customer[c1] does not have project with name[nonexistent_prj_xyz], ",  "values": {}}

Auto-select optimal model (dataframe body) POST

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).

Optimize k-means clustering POST

Schedules an asynchronous job that searches for the best k-means `k` within a given range for the project. Note there is **no `{model}` path segment** — this operates on the project as a whole, not a specific existing model. The request body is an `MLContext` object.