The available options are

• Run till node
• Run from node
• Publish as a model
• Publish code

Task Name

It is the name of the task selected on the workbook canvas.

• You can click the text field to edit or modify the task's name.
• Space between words is not allowed in the Task Name.

Dependent Variable

It allows you to select the dependent variable.

• Only one variable can be selected at a time.

• It is not mandatory to select the dependent variable.

• It is used to determine the confusion matrix and calculate the following KPIs

• Accuracy

• F-score

• Sensitivity

• Specificity

• Error

Independent Variable

It allows you to select Independent variables.

• These are the variables used for anomaly detection, that is, to determine the outliers.
• The independent variable column should not contain any infinite or missing value.
• You can select more than one variable.
• The variable should be of continuous numerical type.

Advanced

Kernel

It allows you to select the kernel function to convert your data into a required form.

• You can select any one of the following kernel functions:
• linear
• poly
• rbf
• sigmoid
• precomputed
• The rbf (Gaussian radial basis function) function is selected by default.

Degree

It allows you to select the degree of the polynomial kernel function.

• It is applicable only when you select poly as the kernel function.
• It is ignored in the case of all the remaining kernels.
• You can insert only integer value for Degree. In the case of any other type of value, you get a validation error.
• Its default value is 3.

Gamma

It allows you to select the gamma parameter.

• Gamma is the kernel coefficient for the following kernel functions.
• rbf
• poly
• sigmoid
• Model behavior is extremely sensitive to the gamma parameter.
• Very small gamma values indicate that the model is constrained (smooth) and cannot capture the data complexity.
• You can select any one of the following gamma parameters:
• scale
• auto
• By default, the scale parameter is selected.
• This parameter is not very significant for anomaly detection.

Coef0

It is used to select the independent term for the kernel function.

• The term is significant in the case of the following kernel functions.
• poly
• sigmoid
• The default value is 0.0.
• This parameter is not very significant for anomaly detection.

Tolerance

It allows you to set the Tolerance value.

• It is the tolerance for the stopping criterion for iterations.
• It is a parameter used while comparing two values during optimization.
• The default value is 0.001.

nu

It allows you to select the value of nu to limit the number of training errors.

• Its value lies between zero and one.
• The default value is 0.5.
• This parameter is significant for anomaly detection.
• It is an upper limit for a fraction of training errors and a lower limit for a fraction of support vectors.
• As the value of nu approaches one (1), the plot gets more scattered.

Cache Size

It allows you to select the cache size.

• It specifies the size of the kernel cache.
• Cache size is the quantum of memory allotted to the kernel cache maintained in memory.
• It improves the building time of the model.
• It is measured in megabytes (MB).
• The default value is 200.0.

Dimensionality Reduction

It allows you to select the dimensionality reduction technique.

• The default value is None.
• Only one data field can be selected.
• The available options are,
• None
• PCA
• Principal Component Analysis (PCA) maps the data linearly to a lower-dimensional space to maximize the variance of the data in the low-dimensional representation.

Node Configuration

It allows you to select the instance of the AWS server to provide control over the execution of a task in a workbook or workflow.

For more details, refer to Worker Node Configuration.