ShapInterpretation: ShapInterpretation

Description Usage Arguments Value

View source: R/fastinference.R

Description

Base interpereter to use the 'SHAP' interpretation library

Usage

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ShapInterpretation(
  learn,
  test_data = NULL,
  link = "identity",
  l1_reg = "auto",
  n_samples = 128
)

Arguments

learn

learner/model

test_data

should be either a Pandas dataframe or a TabularDataLoader. If not, 100 random rows of the training data will be used instead.

link

link can either be "identity" or "logit". A generalized linear model link to connect the feature importance values to the model output. Since the feature importance values, phi, sum up to the model output, it often makes sense to connect them to the ouput with a link function where link(outout) = sum(phi). If the model output is a probability then the LogitLink link function makes the feature importance values have log-odds units.

l1_reg

can be an integer value representing the number of features, "auto", "aic", "bic", or a float value. The l1 regularization to use for feature selection (the estimation procedure is based on a debiased lasso). The auto option currently uses "aic" when less that 20 space is enumerated, otherwise it uses no regularization.

n_samples

can either be "auto" or an integer value. This is the number of times to re-evaluate the model when explaining each predictions. More samples leads to lower variance estimations of the SHAP values

Value

None


fastai documentation built on Oct. 25, 2021, 5:08 p.m.