This section explains each of the properties in the configuration blade.
This associates the Agent to a specific Collection (default to that of the current Data Stream).
The algorithm used to train the model.
The training file used to train the algorithm.
Tick to auto-populate the feature names from the first row of the training file, or the features are named column0, column1, etc.
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.
The following list contains definitions of all parameters, but the combination of parameters used by each algorithm differs.
L1 penalty equal to the absolute value of the magnitude of coefficients.
L2 penalty which is equal to the square of the magnitude of coefficients.
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.
Indicates the number of times the fitting procedure is executed on the training data.
Set this to a whole number for repeatable/deterministic results across multiple trainings.
Before configuring the Agent, please ensure that its input endpoint is connected to a parent Agent which will be sending data to it.
Each parent Agent attribute is mapped against a feature in the training file so that test data matches the training data.
This endpoint is used to receive data from the parent Agent.
Events received from the parent Agent are assigned a class value and made available to this endpoint.