model_performance {auditor} | R Documentation |
Creates auditor_model_performance
object that can be used to plot radar with ranking of models.
model_performance(
object,
score = c("mae", "mse", "rec", "rroc"),
new_score = NULL,
data = NULL,
...
)
modelPerformance(
object,
score = c("mae", "mse", "rec", "rroc"),
new_score = NULL
)
object |
An object of class |
score |
Vector of score names to be calculated. Possible values: |
new_score |
A named list of functions that take one argument: object of class 'explainer' and return a numeric value. The measure calculated by the function should have the property that lower score value indicates better model. |
data |
New data that will be used to calculate scores. Pass |
... |
Other arguments dependent on the score list. |
An object of the class auditor_model_performance
.
score_acc
, score_auc
, score_cooksdistance
, score_dw
,
score_f1
, score_gini
,
score_halfnormal
, score_mae
, score_mse
,
score_peak
, score_precision
, score_r2
,
score_rec
, score_recall
, score_rmse
,
score_rroc
, score_runs
, score_specificity
,
score_one_minus_acc
, score_one_minus_auc
, score_one_minus_f1
,
score_one_minus_precision
, score_one_minus_gini
,
score_one_minus_recall
, score_one_minus_specificity
data(titanic_imputed, package = "DALEX")
# fit a model
model_glm <- glm(survived ~ ., family = binomial, data = titanic_imputed)
# use DALEX package to wrap up a model into explainer
glm_audit <- audit(model_glm,
data = titanic_imputed,
y = titanic_imputed$survived)
# validate a model with auditor
library(auditor)
mp <- model_performance(glm_audit)
mp
plot(mp)