Classification
Last updated: 06 Jan 2023
Description¶
The Classification node is used to apply a classification model to a data set. Classification can be on text and non-text data.
Configuration Options¶
Basic Configuration Options¶
| Setting | Description\Parameters |
|---|---|
Split Columns |
The selected column(s) will be used to split the data set over to build additional models. |
Model Type |
The model type. Options |
- LogisticRegression
- RandomForest
- GradientBoostedTrees
Model Action| RebuildAndScore will automatically rebuild the model everytime you score. Options
- Score
- RebuildAndScore
|
|Target|The column we are predicting; can be continous or categorical in nature. |
|Role Column| Splits input data into SCORING or TRAINING. Builds the model from TRAINING data. |
|Predictors| The columns we are using to predict. |
Advanced Configuration Options¶
Random Forest model specific settings
| Setting | Description |
|---|---|
Output Local Variable Importance |
Output local variable importance (for binary targets only, 0/1). |
Number of Top Local Variables |
Number of local variables to output. |
Actions¶
| Action | Description |
|---|---|
Preview |
After configuring the node, the combined result set can be previewed by clicking the Preview button. |