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Solution - Lab: RF train and eval 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.


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

train_rf annotated

Requirement 3: Like, train_lgbm, this step has split_data as a dependency. This creates a branch in our pipeline.


Requirement 5: This cell just prints a diagnostic message. We can skip it in pipeline execution.


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


Requirement 3: This step depends on train_rf (uses the value of rf_model) and indirectly on split_data because it uses the data values referenced by the variables: x, y, xt, and yt.