Hands-On Deep Learning with R

Summary

In this chapter, we referred to a raw dataset, explored the data, and took the necessary preprocessing steps to get the data ready for modeling. We performed data type transformations to convert numbers and dates being stored as character strings into numeric and date value columns, respectively. In addition, we performed some feature engineering by breaking up the date value into its component parts. After completing preprocessing, we modeled our data. We followed an approach that included creating a baseline model and then tuning hyperparameters to improve our initial score. We used early stopping rounds and grid searches to identify hyperparameter values that produced the best results. After modifying our model-based results from ...