Model Feature Selection
For using analytics processor in both training and prediction mode, you have to explicitly specify Input Labels and Variables such as Continuous, Categorical and Text.
In case of Isotonic Regression, specify Input Label and Continuous Variable.
In case of K-Means, Input Label is not required, since it is used for clustering issues.
In case of K-Means, Input Label is not required, since it is used for clustering issues.
Provide configuration details for Feature Selection as described below:
Input Label: Input Label signifies the incoming message field, which will be considered as a label field for model training.
Features: User can provide all the continuous, categorical and text variables within the features field.
Drop NAN/Null Records: All the null records within the selected columns will get dropped here.
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