After training with the prediction model, it automatically evaluates the prediction accuracy. This screen provides a summary of the model evaluation.

The prediction accuracy is calculated by comparing the actual results with the predicted results of the prediction model that is created. We found that we can predict the withdrawal with good accuracy from the number of stars at the prediction accuracy level!

Then click Accuracy Details.

Select “Precision” to see a more detailed evaluation. Evaluated values of prediction accuracy from various perspectives and tables and graphs of prediction accuracy are generated. You can scroll down to browse.

We found that the prediction accuracy is high. So why is the prediction accuracy high? Click [Contribution].

This screen allows you to see which variables are effective for prediction and how they are effective. For example, we can see that “Customer Rank” has a high contribution to the withdrawal prediction. In addition, from the content on the right side, you can see that it is easy to withdraw if it is “Bronze”. Then click [Predict].