Hands-On Deep Learning with R

Evaluating results and tuning hyperparameters

Building a recommender system, evaluating its performance, and tuning the hyperparameters is a highly iterative process. Ultimately, the goal is to maximize the model's performance and results. Now that we have built and trained our baseline model, we can monitor and evaluate its performance during the training process using the following code:

# evaluate model resultsplot(history)

This results in the following model performance output:

In the following sections, we will experiment with tuning model parameters to improve its performance.