Introduction to Tuning the Model

Introduction to Tuning the Model

In the previous sections, we learned how to leverage the automated machine learning capabilities of Dataiku DSS to create our first model. Thereafter, we looked at the performance and results of our model in the Result tab. We also applied what we learned from the concept videos by evaluating our model in the hands-on lesson. A logical next step is to try to improve the performance of our model.

Machine learning (ML) is an iterative process. For your ML model to best learn the patterns in your data, you can repeat the training process many times, while making adjustments at each training iteration. This is similar to the way human beings learn. For example, to improve your model performance, you could try different feature handling techniques, or different algorithm optimization methods.

In this section, you will learn about the different options in DSS that you can leverage to iteratively improve your model. Then in the hands-on lesson, you’ll use the Design tab to tune the settings of your model with the goal of improving its performance.

Let’s get started!

Summary

Summary

Introduction to Tuning the Model.