Description Usage Arguments Value Examples
This function shows:
plot for the importance of single variables,
tree that shows importance for every newly expanded group of variables,
clustering tree.
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 35 36 37 38 39 40  calculate_triplot(x, ...)
## S3 method for class 'explainer'
calculate_triplot(
x,
type = c("predict", "model"),
new_observation = NULL,
N = 1000,
loss_function = DALEX::loss_root_mean_square,
B = 10,
fi_type = c("raw", "ratio", "difference"),
clust_method = "complete",
cor_method = "spearman",
...
)
## Default S3 method:
calculate_triplot(
x,
data,
y = NULL,
predict_function = predict,
label = class(x)[1],
type = c("predict", "model"),
new_observation = NULL,
N = 1000,
loss_function = DALEX::loss_root_mean_square,
B = 10,
fi_type = c("raw", "ratio", "difference"),
clust_method = "complete",
cor_method = "spearman",
...
)
## S3 method for class 'triplot'
print(x, ...)
model_triplot(x, ...)
predict_triplot(x, ...)

x 
an explainer created with the 
... 
other parameters 
type 
if 
new_observation 
selected observation with columns that corresponds to variables used in the model, should be without target variable 
N 
number of rows to be sampled from data
NOTE: Small 
loss_function 
a function that will be used to assess variable
importance, if 
B 
integer, number of permutation rounds to perform on each variable
in feature importance calculation, if 
fi_type 
character, type of transformation that should be applied for
dropout loss, if 
clust_method 
the agglomeration method to be used, see

cor_method 
the correlation method to be used see

data 
dataset, it will be extracted from 
y 
true labels for 
predict_function 
predict function, it will be extracted from 
label 
name of the model. By default it's extracted from the 'class' attribute of the model. 
triplot object
1 2 3 4 5 6 7 8 9 10 11 12 13 14  library(DALEX)
set.seed(123)
apartments_num < apartments[,unlist(lapply(apartments, is.numeric))]
apartments_num_lm_model < lm(m2.price ~ ., data = apartments_num)
apartments_num_new_observation < apartments_num[30, ]
explainer_apartments < explain(model = apartments_num_lm_model,
data = apartments_num[,1],
y = apartments_num[, 1],
verbose = FALSE)
apartments_tri < calculate_triplot(x = explainer_apartments,
new_observation =
apartments_num_new_observation[1])
apartments_tri

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