This topic describes how to evaluate models by using a binary classification model.

Prerequisites

Algorithm modeling is complete. For more information, see Generate a model by using an algorithm.

Procedure

  1. Go to a Machine Learning Studio project.
    1. Log on to the PAI console.
    2. In the left-side navigation pane, choose Model Training > Studio-Modeling Visualization.
    3. In the upper-left corner of the page, select the region that you want.
    4. Optional:In the search box on the PAI Visualization Modeling page, enter the name of a project to search for the project.
    5. Find the project that you want and click Machine Learning in the Operation column.
  2. Drag components to the canvas to create an experiment.
    1. In the left-side navigation pane, click Components.
    2. In the Components pane, choose Machine Learning > Recommendation. Then, drag the Prediction component to the canvas.
    3. In the Components pane, choose Machine Learning > Evaluation. Then, drag the 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.Model evaluation
    4. 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.
  3. In the upper part of the canvas, click Run.
  4. View the model evaluation report.
    1. After the experiment stops running, right-click the Binary Classification Evaluation component. In the shortcut menu that appears, click View Evaluation Report.
    2. Click the Charts tab, and view the receiver operating characteristic curve (ROC) of the binary classification model with different parameters.Model evaluation