Exercise

Evaluating the L2 regularization model

The final step in this model is to now evaluate how the model performs on the testing data. In other words, what percent of shoppers' behavior are we correctly predicting? Has the performance of the model increased now that you've applied a regularization?

Here, you'll evaluate your model based on accuracy and precision. Remember that accuracy is a measure of how close your experimental measurements agree with known values, while loss is a measure of model error. As such, evaluating both metrics adds value when deciding how well your model performs.

Instructions

100 XP

Evaluate the model_lesson1 model.
Call the accuracy (acc) and loss (loss) of the model.