explain  R Documentation 
Once an explainer has been created using the lime()
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()
.
## S3 method for class 'data.frame' explain( x, explainer, labels = NULL, n_labels = NULL, n_features, n_permutations = 5000, feature_select = "auto", dist_fun = "gower", kernel_width = NULL, gower_pow = 1, ... ) ## S3 method for class 'character' explain( x, explainer, labels = NULL, n_labels = NULL, n_features, n_permutations = 5000, feature_select = "auto", single_explanation = FALSE, ... ) explain( x, explainer, labels, n_labels = NULL, n_features, n_permutations = 5000, feature_select = "auto", ... ) ## S3 method for class 'imagefile' explain( x, explainer, labels = NULL, n_labels = NULL, n_features, n_permutations = 1000, feature_select = "auto", n_superpixels = 50, weight = 20, n_iter = 10, p_remove = 0.5, batch_size = 10, background = "grey", ... )
x 
New observations to explain, of the same format as used when creating the explainer 
explainer 
An 
labels 
The specific labels (classes) to explain in case the model is
a classifier. For classifiers either this or 
n_labels 
The number of labels to explain. If this is given for
classifiers the top 
n_features 
The number of features to use for each explanation. 
n_permutations 
The number of permutations to use for each explanation. 
feature_select 
The algorithm to use for selecting features. One of:

dist_fun 
The distance function to use for calculating the distance
from the observation to the permutations. If 
kernel_width 
The width of the exponential kernel that will be used to
convert the distance to a similarity in case 
gower_pow 
A modifier for gower distance. The calculated distance will be raised to the power of this value. 
... 
Parameters passed on to the 
single_explanation 
A boolean indicating whether to pool all text in

n_superpixels 
The number of segments an image should be split into 
weight 
How high should locality be weighted compared to colour. High values leads to more compact superpixels, while low values follow the image structure more 
n_iter 
How many iterations should the segmentation run for 
p_remove 
The probability that a superpixel will be removed in each permutation 
batch_size 
The number of explanations to handle at a time 
background 
The colour to use for blocked out superpixels 
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
# Explaining a model and an explainer for it library(MASS) iris_test < iris[1, 1:4] iris_train < iris[1, 1:4] iris_lab < iris[[5]][1] model < lda(iris_train, iris_lab) explanation < lime(iris_train, model) # This can now be used together with the explain method explain(iris_test, explanation, n_labels = 1, n_features = 2)
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