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  3. Designing Machine Learning Workflows in Python

Exercise

Pickles

Finally, it is time for you to push your first model to production. It is a random forest classifier which you will use as a baseline, while you are still working to develop a better alternative. You have access to the data split in training test with their usual names, X_train, X_test, y_train and y_test, as well as to the modules RandomForestClassifier() and pickle, whose methods .load() and .dump() you will need for this exercise.

Instructions

100 XP
  • Fit a random forest classifier to the data. Fix the random seed to 42 ensure that your results are reproducible.
  • Write the model to file using pickle. Open the destination file using the with open(____) as ____ syntax.
  • Now load the model from file into a different variable name, clf_from_file.
  • Store the predictions from the model you loaded into a variable preds.