Description Usage Arguments Value References Examples
View source: R/model_performance.R
Function model_performance()
calculates various performance measures for classification and regression models.
For classification models following measures are calculated: F1, accuracy, recall, precision and AUC.
For regression models following measures are calculated: mean squared error, R squared, median absolute deviation.
1  model_performance(explainer, ..., cutoff = 0.5)

explainer 
a model to be explained, preprocessed by the 
... 
other parameters 
cutoff 
a cutoff for classification models, needed for measures like recall, precision, ACC, F1. By default 0.5. 
An object of the class model_performance
.
It's a list with following fields:
residuals
 data frame that contains residuals for each observation
measures
 list with calculated measures that are dedicated for the task, whether it is regression, binary classification or multiclass classification.
type
 character that specifies type of the task.
Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/
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  # regression
library("ranger")
apartments_ranger_model < ranger(m2.price~., data = apartments, num.trees = 50)
explainer_ranger_apartments < explain(apartments_ranger_model, data = apartments[,1],
y = apartments$m2.price, label = "Ranger Apartments")
model_performance_ranger_aps < model_performance(explainer_ranger_apartments )
model_performance_ranger_aps
plot(model_performance_ranger_aps)
plot(model_performance_ranger_aps, geom = "boxplot")
plot(model_performance_ranger_aps, geom = "histogram")
# binary classification
titanic_glm_model < glm(survived~., data = titanic_imputed, family = "binomial")
explainer_glm_titanic < explain(titanic_glm_model, data = titanic_imputed[,8],
y = titanic_imputed$survived)
model_performance_glm_titanic < model_performance(explainer_glm_titanic)
model_performance_glm_titanic
plot(model_performance_glm_titanic)
plot(model_performance_glm_titanic, geom = "boxplot")
plot(model_performance_glm_titanic, geom = "histogram")
# multilabel classification
HR_ranger_model < ranger(status~., data = HR, num.trees = 50,
probability = TRUE)
explainer_ranger_HR < explain(HR_ranger_model, data = HR[,6],
y = HR$status, label = "Ranger HR")
model_performance_ranger_HR < model_performance(explainer_ranger_HR)
model_performance_ranger_HR
plot(model_performance_ranger_HR)
plot(model_performance_ranger_HR, geom = "boxplot")
plot(model_performance_ranger_HR, geom = "histogram")

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