individual_surrogate_model

0th

Percentile

LIME-like explanations based on Ceteris Paribus curves

This function fits a LIME-type explanation of a single prediction. Interpretable binary features that describe the local impact of features on the prediction are created based on Ceteris Paribus Profiles. Thend, a new dataset of similar observations is created and black box model predictions (scores in case of classification) are calculated for this dataset and LASSO regression model is fitted to them. This way, explanations are simplified and include only the most important features. More details about the methodology can be found in the vignettes.

Usage
individual_surrogate_model(x, new_observation, size, seed = NULL,
  kernel = identity_kernel, sampling = "uniform", grid_points = 101)
Arguments
x

an explainer created with the function DALEX::explain().

new_observation

an observation to be explained. Columns in should correspond to columns in the data argument to x.

size

number of similar observation to be sampled.

seed

If not NULL, seed will be set to this value for reproducibility.

kernel

Kernel function which will be used to weight simulated observations.

sampling

Parameter that controls sampling while creating new observations.

grid_points

Number of points to use while calculating Ceteris Paribus profiles.

Value

data.frame of class local_surrogate_explainer

Aliases
  • individual_surrogate_model
Examples
# NOT RUN {
# Example based on apartments data from DALEX package.
library(DALEX)
library(randomForest)
library(localModel)
data('apartments')
mrf <- randomForest(m2.price ~., data = apartments, ntree = 50)
explainer <- explain(model = mrf,
                     data = apartments[, -1])
model_lok <- individual_surrogate_model(explainer, apartments[5, -1],
                                        size = 500, seed = 17)
model_lok
plot(model_lok)

# }
Documentation reproduced from package localModel, version 0.3.11, License: GPL

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