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.
a model to be explained, preprocessed by the
a cutoff for classification models, needed for measures like recall, precision, ACC, F1. By default 0.5.
An object of the class
Explanatory Model Analysis. Explore, Explain and Examine Predictive Models. https://pbiecek.github.io/ema/
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## Not run: library("ranger") titanic_ranger_model <- ranger(survived~., data = titanic_imputed, num.trees = 100, probability = TRUE) # It's a good practice to pass data without target variable explainer_ranger <- explain(titanic_ranger_model, data = titanic_imputed[,-8], y = titanic_imputed$survived) # resulting dataframe has predicted values and residuals mp_ex_rn <- model_performance(explainer_ranger) titanic_glm_model <- glm(survived~., data = titanic_imputed, family = "binomial") explainer_glm <- explain(titanic_glm_model, data = titanic_imputed[,-8], y = titanic_imputed$survived, predict_function = function(m,x) predict.glm(m,x,type = "response"), label = "glm") mp_ex_glm <- model_performance(explainer_glm) mp_ex_glm plot(mp_ex_glm) plot(mp_ex_glm, mp_ex_rn) titanic_lm_model <- lm(survived~., data = titanic_imputed) explainer_lm <- explain(titanic_lm_model, data = titanic_imputed[,-8], y = titanic_imputed$survived) mp_ex_lm <- model_performance(explainer_lm) plot(mp_ex_lm) plot(mp_ex_glm, mp_ex_rn, mp_ex_lm) ## End(Not run)
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