Tuning Hyperparameters in DataRobot

This post was originally part of the DataRobot Community. Visit now to browse discussions and ask questions about DataRobot, AI Cloud, data science, and more.

This article explains DataRobot tools for model hyperparameter tuning.

Advanced Tuning

DataRobot will automatically search for the best hyperparameters to optimize your models. If you wish to investigate the best hyperparameters, or if you wish to change them and tweak them in search for a better model, start by clicking on that model in the Leaderboard.

Then, click on Evaluate > Advanced Tuning.

If you followed the instructions properly, you should be looking at something similar to Figure 2.

At the top, you have three options to choose from:

• New Search
• Searched
• Best of Searched

New Search

The New Search option is for those data scientists looking to initiate a new search of best parameters. The parameters are split into two groups. You have preprocessing parameters like missing value imputation and then you have model parameters like number of trees for example. Click on the parameters you want to change and just type in the value you would like to try out. In some cases, you will even be able to put multiple values in the form of a list.

When you are done, go to the bottom of the menu where you will be able to initiate a Smart Search or a Brute Force search as seen in Figure 3. Please keep in mind that extensive searches can take a long time to compute.

Searched

By clicking on Searched, you will be able to see all of the values DataRobot tried out when searching for the best set of hyperparameters.

Best of Searched

Best of Searched will yield just the best parameters based on the search that DataRobot conducted.

More Information

If you’d like to find out more about DataRobot’s robust hyperparameter tuning capabilities, please visit our public documentation repository and navigate to the Advanced Tuning section.

About the author

Linda Haviland

Community Manager

Meet