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Lab: Create a train_rf and eval_rf steps

Following a process similar to what we did for the LGBM regression model in the previous section, reorganize the code and apply the appropriate annotations for the RandomForest (RF) model. For this lab, the code you will work with is found in this cell.

rf cell


Reorganize and annotate the code for the RF model to meet the following requirements:

  1. Create a new pipeline step called train_rf to train the RF model.
  2. Create a new pipeline step called eval_rf to evaluate the RF model.
  3. Specify the correct dependency relationships for both steps. Note that the train_rf step begins a branch in our pipeline. This branch can run in parallel with the branch for the LGBM model.
  4. For each step, include only cells that contain code that is core to the step.
  5. Exclude cells that are not core to one step or the other using the Skip Cell annotation.


When you are finished, compare your notebook to the solution and make any necessary changes so that your notebook matches the solution.