ALE {cito}

R Documentation

Accumulated Local Effect Plot (ALE)

Description

Performs an ALE for one or more features.

Usage

ALE(
  model,
  variable = NULL,
  data = NULL,
  K = 10,
  type = c("equidistant", "quantile")
)

Arguments

model	a model created by dnn
variable	variable as string for which the PDP should be done
data	data on which ALE is performed on, if NULL training data will be used.
K	number of neighborhoods original feature space gets divided into
type	method on how the feature space is divided into neighborhoods.

Details

If the defined variable is a numeric feature, the ALE is performed. Here, the non centered effect for feature j with k equally distant neighborhoods is defined as:

\hat{\tilde{f}}_{j,ALE}(x)=\sum_{k=1}^{k_j(x)}\frac{1}{n_j(k)}\sum_{i:x_{j}^{(i)}\in{}N_j(k)}\left[\hat{f}(z_{k,j},x^{(i)}_{\setminus{}j})-\hat{f}(z_{k-1,j},x^{(i)}_{\setminus{}j})\right]

Where N_j(k) is the k-th neighborhood and n_j(k) is the number of observations in the k-th neighborhood.

The last part of the equation, \left[\hat{f}(z_{k,j},x^{(i)}_{\setminus{}j})-\hat{f}(z_{k-1,j},x^{(i)}_{\setminus{}j})\right] represents the difference in model prediction when the value of feature j is exchanged with the upper and lower border of the current neighborhood.

Value

A list of plots made with 'ggplot2' consisting of an individual plot for each defined variable.

See Also

PDP

Examples

if(torch::torch_is_installed()){
library(cito)

# Build and train  Network
nn.fit<- dnn(Sepal.Length~., data = datasets::iris)

ALE(nn.fit, variable = "Petal.Length")
}

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