Description Usage Arguments Value Author(s) References See Also Examples
This function allows to draw the lift effect on a graph for binary classification model
1 | lift_effect(predictions, true_labels, positive_label)
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predictions |
a vector of predictions. These are generally the result of a machine learning model. The predictions must be probabilities (a real number between 0 and 1). |
true_labels |
a vector of true labels. |
positive_label |
a character or integer that specify the positive label (Y=1) in the 'true_labels'. |
a ggplot object containing the lift effect.
Simon CORDE
Link to the author's github package repository: https://github.com/Redcart/helda
lift_curve
1 2 3 4 5 6 7 8 9 | data_training <- titanic_training
data_validation <- titanic_validation
model_glm <- glm(formula = "Survived ~ Pclass + Sex + Age + SibSp + Fare + Embarked",
data = data_training,
family = binomial(link = "logit"))
predictions <- predict(object = model_glm, newdata = data_validation, type = "response")
plot <- lift_effect(predictions = predictions, true_labels = data_validation$Survived,
positive_label = 1)
plot
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