Description Usage Arguments Value Examples
This explainer works for individual observations. For each observation it calculates Individual Variable Profiles for selected variables. For this reason it is also called 'Local Profile Plot'.
1 2 3 4 5 6 7 8 9 10 11 12 | individual_variable_profile(x, ...)
## S3 method for class 'explainer'
individual_variable_profile(x, new_observation,
y = NULL, variables = NULL, variable_splits = NULL,
grid_points = 101, ...)
## Default S3 method:
individual_variable_profile(x, data,
predict_function = predict, new_observation, y = NULL,
variables = NULL, variable_splits = NULL, grid_points = 101,
label = class(x)[1], ...)
|
x |
a model to be explained, or an explainer created with function 'DALEX::explain()'. |
... |
other parameters |
new_observation |
a new observation with columns that corresponds to variables used in the model |
y |
true labels for 'new_observation'. If specified then will be added to ceteris paribus plots. |
variables |
names of variables for which profiles shall be calculated. Will be passed to 'calculate_variable_splits()'. If NULL then all variables from the validation data will be used. |
variable_splits |
named list of splits for variables, in most cases created with 'calculate_variable_splits()'. If NULL then it will be calculated based on validation data avaliable in the 'explainer'. |
grid_points |
number of points for profile. Will be passed to 'calculate_variable_splits()'. |
data |
validation dataset, will be extracted from ‘x' if it’s an explainer |
predict_function |
predict function, will be extracted from ‘x' if it’s an explainer |
label |
name of the model. By default it's extracted from the 'class' attribute of the model |
An object of the class 'ceteris_paribus_explainer'. It's a data frame with calculated average responses.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | library("DALEX2")
library("ceterisParibus2")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf,
data = apartments_test[,2:6], y = apartments_test$m2.price)
my_apartment <- apartments_test[1, ]
lp_rf <- individual_variable_profile(explainer_rf, my_apartment)
lp_rf
plot(lp_rf)
# --------
# multiclass
HR_rf <- randomForest(status ~ . , data = HR)
explainer_rf <- explain(HR_rf, data = HRTest, y = HRTest)
my_HR <- HRTest[1, ]
lp_rf <- individual_variable_profile(explainer_rf, my_HR)
lp_rf
plot(lp_rf, color = "_label_")
## End(Not run)
|
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