This topic describes how to evaluate models by using a binary classification model.
Algorithm modeling is complete. For more information, see Generate a model by using an algorithm.
- Go to a Machine Learning Studio project.
- Log on to the PAI console.
- In the left-side navigation pane, choose .
- In the upper-left corner of the page, select the region that you want.
- Optional:In the search box on the PAI Visualization Modeling page, enter the name of a project to search for the project.
- Find the project that you want and click Machine Learning in the Operation column.
- Drag components to the canvas to create an experiment.
- In the left-side navigation pane, click Components.
- In the Components pane, choose Prediction component to the canvas.. Then, drag the
- In the Components pane, choose Binary Classification Evaluation component to the canvas and connect it to the components prepared during algorithm modeling. For more information, see Generate a model by using an algorithm. The following figure shows how to connect the components.. Then, drag the
- Click the Binary Classification Evaluation component on the canvas. On the Fields Setting tab on the right side, set the Original Label Column parameter to ifhealth.
- In the upper part of the canvas, click Run.
- View the model evaluation report.
- After the experiment stops running, right-click the Binary Classification Evaluation component. In the shortcut menu that appears, click View Evaluation Report.
- Click the Charts tab, and view the receiver operating characteristic curve (ROC) of the binary classification model with different parameters.