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Solution - 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.


Requirements 1, 4: Following the example for the XGB model, we need to isolate the code that creates and trains the XGB model in a single cell. Requirements 1 and 4 are addressed in the code depicted in this figure.

xgb step

Requirement 3: Like, train_lgbm and train_xgb, this step has split_data as a dependency.

Requirement 5: There are no cells to skip for this lab.

Requirements 2, 4: The code for the eval_xgb step is depicted in the figure below. We’ll leave the print statements together with the evaluation code, because we do want to output the result as part of the last step for this branch of our pipeline.

xgb step

Requirement 3: This step depends on train_xgb (uses the value of xgb_grid) and indirectly on split_data because it uses the data values referenced by the variables: xt and yt.