View source: R/model_performance.R
model_performance | R Documentation |
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.
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/
# 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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