explain: Explain model predictions

In viadee/anchorsOnR: AnchorsOnR: High-Precision Model-Agnostic Explanations in R

Description

Once an explainer has been created using the [anchors()] function it can be used to explain the result of the model on new observations. The 'explain()' function takes new observation along with the explainer and returns a data.frame with prediction explanations, one observation per row. The returned explanations can then be visualised in a number of ways, e.g. with [plot_features()].

This scenario is described by the previously created anchors.tabular.

Usage

explain(x, explainer, labels = NULL, ...)

## S3 method for class 'data.frame'
explain(x, explainer, labels = NULL, ...)

Arguments

x	New observations to explain, of the same format as used when creating the explainer
explainer	An 'explainer' object to use for explaining the observations
labels	The specific labels (classes) to explain in case the model is a classifier.
...	Parameters passed on to the 'predict_model()' method

Value

A data.frame encoding the explanations one row per explained observation. The columns are:

- 'model_type': The type of the model used for prediction. - 'case': The case being explained (the rowname in 'cases'). - 'model_r2': The quality of the model used for the explanation - 'model_intercept': The intercept of the model used for the explanation - 'model_prediction': The prediction of the observation based on the model used for the explanation. - 'feature': The feature used for the explanation - 'feature_value': The value of the feature used - 'feature_weight': The weight of the feature in the explanation - 'feature_desc': A human readable description of the feature importance. - 'data': Original data being explained - 'prediction': The original prediction from the model

Furthermore classification explanations will also contain:

- 'label': The label being explained - 'label_prob': The probability of 'label' as predicted by 'model'

Examples

# Explaining a model based on tabular data
library(anchors)
library(mlr)
data(iris)
# our goal is to predict the species
task = makeClassifTask(data = iris, target = "Species", id = "iris")
# setting up a learner
lrn = makeLearner("classif.lda")
# train the learner on the training set
model = train(learner = lrn, task = task)
explainer = anchors(iris, model, target = "Species")
explanations = explain(iris[100,], explainer)
# Explaining a model based on tabular data
library(anchors)
library(mlr)
data(iris)
# our goal is to predict the species
task = makeClassifTask(data = iris, target = "Species", id = "iris")
# setting up a learner
lrn = makeLearner("classif.lda")
# train the learner on the training set
model = train(learner = lrn, task = task)
explainer = anchors(iris, model, target = "Species")
explanations = explain(iris[100,], explainer)

viadee/anchorsOnR documentation built on Nov. 22, 2019, 5:24 p.m.