1. Learn
    2. /
  2. Courses
    3. /
  3. Introduction to MLflow

Exercise

Logging a run

In this exercise, you will train a model using scikit-learn's Linear Regression to predict profit from the Unicorn dataset. You have created an experiment called Unicorn Sklearn Experiment and started a new run. You will log metrics for r2_score and parameters for n_jobs as well as log the training code as an artifact.

The Linear Regression model will be trained with n_jobs parameter set to 1. The r2_score metric will be produced using the r2_score() from scikit-learn based on y_pred variable which came from predictions of X_test.

model = LinearRegression(n_jobs=1)
model.fit(X_train, y_train)

y_pred = model.predict(X_test)

The mlflow module as well as the LinearRegression, train_test_split, and metrics modules from scikit-learn will be imported.

Instructions

100 XP
  • Log the r2_score variable as a metric called "r2_score".
  • Log a parameter called "n_jobs" to the Tracking Server.
  • Log the "train.py" file as an artifact in the run.