Classification
Classification
Notes: The Reader (Dataset) should be connected to the algorithm. Missing values should not be present in any rows or columns of the reader. To find out missing values in a data, use Descriptive Statistics. Refer to Descriptive Statistics. If missing ...
AdaBoost in Classification
You can find AdaBoost under the Machine Learning section in the Classification category on Feature Studio. Alternatively, use the search bar to find the AdaBoost algorithm. Use the drag-and-drop method or double-click to use the algorithm in the ...
Binomial Logistic Regression
Binomial Logistic Regression is located under Machine Learning () in Data Classification, in the task pane on the left. Use drag-and-drop method to use the algorithm in the canvas. Click the algorithm to view and select different properties for ...
Categorical Naive Bayes
The categorical Naive Bayes test is located under Machine Learning ( ) in Classification, on the left task pane. Alternatively, use the search bar for finding the Categorical Naive Bayes test feature. Use the drag-and-drop method or double-click to ...
Decision Tree
Decision Tree is located under Machine Learning ( ) in Classification, in the task pane on the left. Use drag-and-drop method to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis. Refer to ...
Extreme Gradient Boost Classification (XGBoost)
Extreme Gradient Boost is located under Machine Learning () in Classification, in the task pane on the left. Use the drag-and-drop method (or double-click on the node) to use the algorithm in the canvas. Click the algorithm to view and select ...
Gradient Boosting in Classification
The category Gradient Boosting is located under Machine Learning in Classification on the feature studio. Alternatively, use the search bar to find the Gradient Boosting test feature. Use the drag-and-drop method or double-click to use the algorithm ...
Random Forest
Random Forest is located under Machine Learning ( ) in Classification, in the left task pane. Use the drag-and-drop method (or double-click on the node) to use the algorithm in the canvas. Click the algorithm to view and select different properties ...
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