This associates the Agent to a specific Collection (default to that of the current Data Stream).

Refer to Collections and Stream Hosts to understand more about Collections.



Learning Algorithm

The algorithm used to train the model.


The dataset file used to train the algorithm.

Has Header

Tick to auto-populate the feature names from the first row of the training file, or the features are named column0, column1, etc.

Separator Character

The character used to separate data values in the training file.

Features (Name, Type, and Variable Type)

The grid of features is categorized by name, data type, and variable type.

  • Name: Feature name

  • Type: Data type of the feature

  • Variable Type: options are feature (default), exclude (ignore certain features from training), or class variable (the output).

Algorithm Parameters

The following list contains definitions of all parameters, but the combination of parameters used by each algorithm differs.


History Size

The number of previous iterations to remember for prediction.

L1 Regularization

L1 penalty equal to the absolute value of the magnitude of coefficients.

L2 Regularization

L2 penalty which is equal to the square of the magnitude of coefficients.

Learning Rate

The amount of change in model in each iteration for better prediction.

Number of Interations

The number of times algorithms parameters are updated in a single run.

Number of Trees

The total number of trees generated on training data.

Number of Leaves

The number of leaves per tree in the model.

Minimum Example Count Per Leaf

The minimum number of samples required to be at a leaf node.

Model Options


Cross-Validation Folds

Indicates the number of time fitting procedure is executed on training data

Deterministic Seed

Set this to a whole number for repeatable/deterministic results across multiple trainings




This endpoint is used to receive data from the parent Agent


Events received by the parent Agent and predicted values are made available to this endpoint.

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