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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 has been 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)

r2_score = r2_score(y_test, y_pred)

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

This exercise is part of the course

Introduction to MLflow

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Exercise instructions

  • 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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Log the metric r2_score as "r2_score"
____.____("____", ____)

# Log parameter n_jobs as "n_jobs"
____.____("____", ____)

# Log the training code
____.____("train.py")
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