Zoho · Schema

CreateAutoMLAnalysisConfig

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Properties

Name	Type	Description
name	string	Name of the analysis template.
description	string	Detailed description of the analysis template.
trainingTableId	integer	Unique ID of the training table used to build the analysis model.
targetColumn	string	Name of the target column from the training table that the model will predict.
predictionType	string	Type of prediction to be performed by the model.
features	array	List of column names from the training table to use as input features.
serverOption	integer	Specifies the server memory option.
algorithms	object	Configuration for algorithms to be used for training.

View JSON Schema on GitHub

JSON Schema

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://raw.githubusercontent.com/api-evangelist/zoho/main/json-schema/zoho-create-auto-mlanalysis-config-schema.json",
  "title": "CreateAutoMLAnalysisConfig",
  "x-generated": "2026-10-04",
  "x-method": "derived",
  "x-generator": "derive-json-schema.py",
  "x-source": "openapi/zoho-modeling-apis-api-openapi.yml#/components/schemas/CreateAutoMLAnalysisConfig",
  "type": "object",
  "properties": {
    "name": {
      "type": "string",
      "description": "Name of the analysis template.",
      "maxLength": 60
    },
    "description": {
      "type": "string",
      "description": "Detailed description of the analysis template.",
      "maxLength": 1000
    },
    "trainingTableId": {
      "type": "integer",
      "format": "int64",
      "description": "Unique ID of the training table used to build the analysis model."
    },
    "targetColumn": {
      "type": "string",
      "description": "Name of the target column from the training table that the model will predict."
    },
    "predictionType": {
      "type": "string",
      "description": "Type of prediction to be performed by the model.",
      "enum": [
        "REGRESSION",
        "CLASSIFICATION",
        "CLUSTERING"
      ]
    },
    "features": {
      "type": "array",
      "description": "List of column names from the training table to use as input features.",
      "items": {
        "type": "string",
        "description": "Each value must be a valid column name."
      },
      "minItems": 1,
      "maxItems": 100
    },
    "serverOption": {
      "type": "integer",
      "description": "Specifies the server memory option.",
      "enum": [
        1,
        2,
        3
      ]
    },
    "algorithms": {
      "type": "object",
      "description": "Configuration for algorithms to be used for training.",
      "properties": {
        "decisionTreeRegression": {
          "type": "object",
          "properties": {
            "minimumSampleSplit": {
              "type": "integer"
            },
            "maximumDepth": {
              "type": "integer"
            }
          }
        },
        "randomForestRegression": {
          "type": "object",
          "properties": {
            "minimumSampleSplit": {
              "type": "integer"
            },
            "maximumDepth": {
              "type": "integer"
            },
            "numberOfTrees": {
              "type": "integer"
            }
          }
        },
        "olsRegression": {
          "type": "object",
          "properties": {
            "intercept": {
              "type": "boolean"
            },
            "allowAlternateModel": {
              "type": "boolean"
            }
          }
        },
        "lassoRegression": {
          "type": "object",
          "properties": {
            "tolerance": {
              "type": "number"
            },
            "shrinkage": {
              "type": "integer"
            },
            "maximumIterations": {
              "type": "integer"
            }
          }
        },
        "ridgeRegression": {
          "type": "object",
          "properties": {
            "shrinkage": {
              "type": "integer"
            }
          }
        },
        "svmRegressor": {
          "type": "object",
          "properties": {
            "epsilon": {
              "type": "number"
            },
            "softMargin": {
              "type": "number"
            },
            "tolerance": {
              "type": "number"
            },
            "epochs": {
              "type": "integer"
            }
          }
        },
        "gradientBoostingRegression": {
          "type": "object",
          "properties": {
            "lossFunction": {
              "type": "string",
              "enum": [
                "LEASTSQUARES",
                "QUANTILE",
                "LEASTABSOLUTEDEVIATION",
                "HUBER"
              ]
            },
            "maximumDepth": {
              "type": "integer"
            },
            "maximumNodes": {
              "type": "integer"
            },
            "nodeSize": {
              "type": "integer"
            },
            "numberOfTrees": {
              "type": "integer"
            },
            "shrinkage": {
              "type": "number"
            },
            "subSample": {
              "type": "number"
            }
          }
        },
        "gradientBoostingClassification": {
          "type": "object",
          "properties": {
            "lossFunction": {
              "type": "string",
              "enum": [
                "LEASTSQUARES",
                "QUANTILE",
                "LEASTABSOLUTEDEVIATION",
                "HUBER"
              ]
            },
            "maximumDepth": {
              "type": "integer"
            },
            "maximumNodes": {
              "type": "integer"
            },
            "nodeSize": {
              "type": "integer"
            },
            "numberOfTrees": {
              "type": "integer"
            },
            "shrinkage": {
              "type": "number"
            },
            "subSample": {
              "type": "number"
            }
          }
        },
        "adaptiveBoost": {
          "type": "object",
          "properties": {
            "maximumDepth": {
              "type": "integer"
            },
            "maximumNodes": {
              "type": "integer"
            },
            "nodeSize": {
              "type": "integer"
            },
            "numberOfTrees": {
              "type": "integer"
            }
          }
        },
        "decisionTreeClassification": {
          "type": "object",
          "properties": {
            "minimumSampleSplit": {
              "type": "integer"
            },
            "maximumDepth": {
              "type": "integer"
            }
          }
        },
        "randomForestClassification": {
          "type": "object",
          "properties": {
            "minimumSampleSplit": {
              "type": "integer"
            },
            "maximumDepth": {
              "type": "integer"
            },
            "numberOfTrees": {
              "type": "integer"
            }
          }
        },
        "logisticRegression": {
          "type": "object",
          "properties": {
            "lambda": {
              "type": "number"
            },
            "maximumIterations": {
              "type": "integer"
            },
            "tolerance": {
              "type": "number"
            }
          }
        },
        "linearDiscriminantAnalysis": {
          "type": "object",
          "properties": {
            "tolerance": {
              "type": "number"
            }
          }
        },
        "maxEntropy": {
          "type": "object",
          "properties": {
            "lambda": {
              "type": "number"
            },
            "maximumIterations": {
              "type": "integer"
            },
            "tolerance": {
              "type": "number"
            }
          }
        },
        "kMeansPP": {
          "type": "object",
          "properties": {
            "clustersCount": {
              "type": "integer"
            },
            "kMax": {
              "type": "integer"
            },
            "chIndexBasedOptimalK": {
              "type": "boolean"
            },
            "calculatePerformanceMetrics": {
              "type": "boolean"
            }
          }
        },
        "kModes": {
          "type": "object",
          "properties": {
            "clustersCount": {
              "type": "integer"
            },
            "minimumClusterCount": {
              "type": "integer"
            },
            "maximumClusterCount": {
              "type": "integer"
            },
            "dissimilarityMeasure": {
              "type": "string",
              "enum": [
                "BINARY",
                "GLOBAL_FREQUENCY",
                "RELATIVE_FREQUENCY",
                "JARO_WINKLER",
                "LEVENSHTEIN",
                "JACCARD"
              ]
            }
          }
        },
        "kPrototypes": {
          "type": "object",
          "properties": {
            "clustersCount": {
              "type": "integer"
            },
            "kMax": {
              "type": "integer"
            },
            "chIndexBasedOptimalK": {
              "type": "boolean"
            },
            "gamma": {
              "type": "number"
            }
          }
        },
        "xMeans": {
          "type": "object",
          "properties": {
            "kMax": {
              "type": "integer"
            },
            "maximumIterations": {
              "type": "integer"
            },
            "tolerance": {
              "type": "number"
            }
          }
        },
        "gMeans": {
          "type": "object",
          "properties": {
            "kMax": {
              "type": "integer"
            },
            "maximumIterations": {
              "type": "integer"
            },
            "tolerance": {
              "type": "number"
            }
          }
        }
      }
    }
  },
  "required": [
    "name",
    "trainingTableId",
    "predictionType",
    "features",
    "serverOption",
    "algorithms"
  ]
}

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