Identify optimal tree depth

Now you will tune the max_depth parameter of the decision tree to discover the one which reduces over-fitting while still maintaining good model performance metrics. You will run a for loop through multiple max_depth parameter values and fit a decision tree for each, and then calculate performance metrics.

The list called depth_list with the parameter candidates has been loaded for you. The depth_tuning array has been built for you with 2 columns, with the first one being filled with the depth candidates, and the next one being a placeholder for the recall score. Also, the features and target variables have been loaded as train_X, train_Y for the training data, and test_X, test_Y for the test data. Both numpy and pandas libraries are loaded as np and pd respectively.

This exercise is part of the course

Machine Learning for Marketing in Python

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Exercise instructions

Run a for loop over the range from 0 to the length of the list depth_list.
For each depth candidate, initialize and fit a decision tree classifier and predict churn on test data.
For each depth candidate, calculate the recall score by using the recall_score() function and store it in the second column of depth_tunning.
Create a pandas DataFrame out of depth_tuning with the appropriate column names.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Run a for loop over the range of depth list length
for index in ___(0, len(depth_list)):
  # Initialize and fit decision tree with the `max_depth` candidate
  mytree = DecisionTreeClassifier(___=depth_list[index])
  mytree.fit(___, train_Y)
  # Predict churn on the testing data
  pred_test_Y = mytree.predict(___)
  # Calculate the recall score 
  depth_tuning[index,1] = ___(test_Y, ___)

# Name the columns and print the array as pandas DataFrame
col_names = ['Max_Depth','Recall']
print(pd.DataFrame(depth_tuning, columns=___))

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