Second use case – model optimization

In Chapter 1, An Overview of Comet, you built a simple use case that permitted you to define a simple regression model and show the results in Comet. The example used the diabetes dataset provided by the scikit-learn library and calculated the mean squared error (MSE) for different values of seeds.

During the experiment, you will surely have noticed that the average MSE was about 3,000. In this example, we show how to use the concept of Optimizer to reduce the MSE value. Since the linear regression model does not provide any parameters to optimize, in this example, we will build a gradient boosting regressor model, and we will tune some of the parameters it provides.

In this example, we suppose that the code implemented in Chapter 1, An Overview of Comet, for the second use case is running. Thus, please refer to it for further details.

The full code of this example is available in the GitHub repository, at the following link:...