Shows the prediction for a single sample over all possible values that a variable of interest can take.
Sample explanation for a categorical variable.
This explanation shows the prediction for a single sample over all possible values a variable can take.
Information from this explanation can be used for different purposes.
- Evaluate business actions on-the-fly: getting to know what would happen in advance can help you make better decisions. Stop relying on your intuitions.
- Increase profits: validate actions that will increase your profits.
- Model validation: using previously known samples, you can validate whether your model works properly.
- Further insights: get to know the models you are designing.
The intuition is quite simple. What is the prediction for every possible value of a variable? Basically, we can create a sample for each potential combination and run the model to get the actual prediction.
- be the model we are trying to explain.
- be the matrix containing input data for the model with samples called
- be the explained variable at the j-th column of X.
The idea is quite simple as presented in Plain English. Formally, we say that the function
that represents these potential predictions for sample
is defined as:
takes all possible values for variable
Goldstein, Alex, Adam Kapelner, Justin Bleich, and Emil Pitkin. 2015. “Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation.” Journal of Computational and Graphical Statistics 24 (1): 44–65. https://doi.org/10.1080/10618600.2014.907095.