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Lab: XGB train and eval steps

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

xgb step


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

  1. Create a new pipeline step called train_xgb to train the XGB model.
  2. Create a new pipeline step called eval_xgb to evaluate the XGB model.
  3. Specify the correct dependency relationships for both steps. Note that the train_xgb step begins a branch in our pipeline. This branch can run in parallel with the branches for the LGBM and RF models.
  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.