Model evaluation

After you’ve built your model, you can evaluate how well your model performed on your data before using it to make predictions. You can use information, such as the model’s accuracy when predicting labels and advanced metrics, to determine whether your model can make sufficiently accurate predictions for your data.

The section Evaluate your model's performance describes how to view and interpret the information on your model's Analyze page. The section Use advanced metrics in your analyses contains more detailed information about the Advanced metrics used to quantify your model’s accuracy.

You can also view more advanced information for specific model candidates, which are all of the model iterations that Canvas runs through while building your model. Based on the advanced metrics for a given model candidate, you can select a different candidate to be the default, or the version that is used for making predictions and deploying. For each model candidate, you can view the Advanced metrics information to help you decide which model candidate you’d like to select as the default. You can view this information by selecting the model candidate from the Model leaderboard. For more information, see View model candidates in the model leaderboard.

Canvas also provides the option to download a Jupyter notebook so that you can view and run the code used to build your model. This is useful if you’d like to make adjustments to the code or learn more about how your model was built. For more information, see Download a model notebook.