CODEWITHSUNDEEP
pythonpython-3.xmachine-learningscikit-learnrandom-forest
user130104
user130104

Reputation: 51

Random Forests interpretability

I have been using the sklearn RandomForestClassifier to solve a binary classification problem.

For a particular sample prediction, I would like to be able to know how to change the features' values to make the prediction change.

E.g. let's say I have an entry with [size = 15, width = 8, height = 13] and the model gives me aprobability = 0.9 to be of class 1. What I would like to be able to say is "change size from 15 to 10" and then your probability=0.1 for example.

Then optimally, what I would like is the smallest "gradient" in the features values to move from one class to another (or the one that gives the most change in probability).

Maybe I'm wrong but from what I've read the packages LIME and TreeInterpreter do not provide this kind of information?

Upvotes: 5

Views: 915

Answers (2)

mahesh ghanta
mahesh ghanta

Reputation: 131

Partial dependence plots approximate the dependence between the target and a particular independent variable marginalised on all other independent variables.

While it doesn't give exact gradient at all points. It helps us get an intuition of the variable behavior.

You can find more about it here: https://scikit-learn.org/stable/modules/generated/sklearn.inspection.plot_partial_dependence.html

Upvotes: 1

Mikedev
Mikedev

Reputation: 326

I know two ways to interpreter a Random Forest:

  • If you use sklearn Random Forest you can use feature_importances_ class attribute (higher is better)
  • A more general way to interpreter a "black box model" is sensitivity analysis and i think in your question you guess this

Upvotes: 0

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