Automating Hyperparameter Tuning: Lessons Learned and Best Practices from Taboola Engineering Blog
Hyperparameter tuning is a crucial step in the machine learning process. It can often mean the difference between a mediocre model and a highly accurate one. At Taboola, we have been working on implementing a hyperparameter tuning script to streamline this process and ensure that we are constantly improving our models.
The initial version of our script was simple, but it encompassed most of our needs. It was easy to run, generated experiments based on JSON input, enriched experiments with metrics, saved results to the cloud, and ultimately led to a significant improvement in our model’s Mean Squared Error (MSE).
As we continued to use the script, we learned more about our models and began to understand which hyperparameter values worked best. We also developed a method to ensure statistical significance in our results by training models on different date ranges.
One challenge we faced was the tradeoff between running more experiments and maintaining reliable results. To address this, we tested different amounts of data and epochs to determine the optimal training setup.
To further automate the process, we added functionality for the script to choose hyperparameter values for us, starting with learning rate related parameters. This helped us focus on finding the best learning rate before tuning other hyperparameters.
In the latest version of our script, we implemented random search to improve the selection of hyperparameters. While grid search may be easier to analyze, random search proved to be more effective in finding better hyperparameters.
Automating the hyperparameter tuning process has been incredibly beneficial for us at Taboola. It has allowed us to run experiments more efficiently, gain a deeper understanding of our models, and continually improve our accuracy. If you are working on a machine learning project, consider implementing a similar automation process to optimize your models and achieve better results.