# gaussian_kernel

0th

Percentile

##### LIME kernel from the original article with sigma = 1.

Since only binary features are used, the weight associated with an observation is simply exp(-{number of features that were changed compared to the original observation}). Kernels are meant to be used as an argument to individual_surrogate_model function. Other custom functions can be used. Such functions take two vectors and return a single number.

##### Usage
gaussian_kernel(explained_instance, simulated_instance)
##### Arguments
explained_instance

explained instance

simulated_instance

new observation

numeric

##### Aliases
• gaussian_kernel
##### Examples
# NOT RUN {
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,
kernel = gaussian_kernel)
# In this case each simulated observation has weight
# that is small when the distance from original observation is large,
# so closer observation have more weight.
model_lok
plot(model_lok)

# }

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

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