Configuration

General
Property
Description
Collection
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.
Training
Property
Description
Learning Algorithm
The algorithm used to train the model.
Dataset
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. 
Property
Description
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
Property
Description
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
Endpoints
Name
Description
Input
This endpoint is used to receive data from the parent Agent
Output
Events received by the parent Agent and predicted values are made available to this endpoint. 

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

Property	Description
Learning Algorithm	The algorithm used to train the model.
Dataset	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).

Property	Description
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.

Property	Description
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

Name	Description
Input	This endpoint is used to receive data from the parent Agent
Output	Events received by the parent Agent and predicted values are made available to this endpoint.

Last modified 1yr ago