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 values are present, impute them using Missing Value Imputation on the dataset. It is under Model Studio, in Data Preparation. Refer to Missing Value Imputation.
  • The dependent or output variable must be a discrete data type.

List of Classification Algorithms

    • Related Articles

    • 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 ...

    • 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 ...

    • 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 ...

    • Publishing Models

      You can publish algorithms as models after their successful execution. A model can be reused in a workbook for training and experimenting or can be used in a workflow for production. Notes: This functionality is available only for Machine Learning ...

    • Model Compare

      Working with Model Compare To start working with Model Compare, follow the steps given below. Go to the Home page and create a new workbook or open an existing workbook. Drag and drop the required dataset on the workbook canvas. In the Properties ...