Exercise

Adding parameters to project run

Parameters can be used to configure the behavior of a model by being passed as variables to the model during training. This allows you to train the model several times using different parameters without modifying the training code itself.

In this exercise, you will use the mlflow projects module to run a Project used to train a Logistic Regression model for your Insurance experiment. You will create code using the mlflow projects module that will run your project. You will then add parameters that will be passed as hyperparameters to the model during training.

Instructions

100 XP

Call mlflow.projects.run() function from the mlflow projects module.
Create the parameters dictionary and set n_jobs to 2 and fit_intercept to False.