Configuration

Binary Classification

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Binary Classification

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Example

Configuration

Release Notes

Configuration

This section explains each of the properties in the configuration blade. 
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 training 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
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 times the fitting procedure is executed on the training data.
Deterministic Seed
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.
Input Mapping
Each parent Agent attribute is mapped against a feature in the training file so that test data matches the training data.
Refer to Setup Input Mappings for step-by-step instructions. 
Endpoints
Name
Description
Input
This endpoint is used to receive data from the parent Agent.
Output
Events received from the parent Agent are assigned a class value and made available to this endpoint.

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Release Notes

Last modified 5mo ago

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