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Disclaimer: Although the Partial Dependence Plot derives from a textbook, and the method in question is mentioned in an exercise, I'm not trying to solve this for a class or homework assignment. I'm trying to develop code for computing of this plots for a real world problem in my company. Regardless, added the self-study tag.

I'm using gradient boosting for a classification task. As this is approach is a little more "Black Boxed" than traditional Logistic Regression for classification, I'm looking into methods that offer some insight on the dependence of the target with my inputs.

In "Elements of Statistical Learning (Hastie, Tibshirani, Friedman)", the authors propose Partial Dependence Plots as a tool for this task. Partial Dependence defines a set of Selected variables $X_s$ and their complement, $X_c$, and gives us the partial dependence function

$f_s(X_s) = E_{X_c}f(X_s,X_c)$

The evaluation of this formula using the training data is given by

$\bar{f}_s(X_s) = \frac{1}{N}\sum\limits_{i=1}^N{f(X_s,x_{ic})}$

This implementation would take a pass over the entire data for each evaluated value of the partial dependence function, which is incredibly expensive.

The book mentions that this evaluation can be done without the data for tree based models, but it leaves it as an exercise (10.11). I've been trying to figure out how for the past week, but I have no clue on how it can be done. I've tried searching the web for an implementation, or even for the solution of the exercise of the book, to no avail.

In short, how can I implement partial dependence plots for tree based models without resorting to the training data?

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  • $\begingroup$ Since this is a textbook exercise, please add the [self-study] tag & read its wiki. $\endgroup$ – Silverfish Sep 21 '16 at 21:07
  • $\begingroup$ @Silverfish Actually this is not a homework or class assignment. I'm trying to develop a tool for application on a real world problem in my company. I'll clarify it on the question. $\endgroup$ – hemagso Sep 21 '16 at 21:14
  • $\begingroup$ Thanks. If you read our wiki you'll see that the [self-study] isn't just for homework or class assignments (after all, how would we know why someone is attempting an exercise?). It's the fact that you're essentially asking for a solution to a textbook exercise (10.11) that renders it self-study, even if you have an application in mind! It's a slightly confusing tag name really. $\endgroup$ – Silverfish Sep 21 '16 at 22:14
  • $\begingroup$ If $N$ is large why not use a subsample of the training data? $\endgroup$ – alexpghayes Aug 27 '18 at 1:31
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If you walk through sklearn implementation of partial dependence routine, you will find the description of the procedure for trees:

    For each row in ``X`` a tree traversal is performed.
    Each traversal starts from the root with weight 1.0.

    At each non-terminal node that splits on a target variable either
    the left child or the right child is visited based on the feature
    value of the current sample and the weight is not modified.
    At each non-terminal node that splits on a complementary feature
    both children are visited and the weight is multiplied by the fraction
    of training samples which went to each child.

    At each terminal node the value of the node is multiplied by the
    current weight (weights sum to 1 for all visited terminal nodes).
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