knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" ) options(tibble.print_min = 5, tibble.print_max = 5)
This package provides functionalities that aim at facilitating and saving time when analysing data.
You can install helda from CRAN by simply running:
install.packages("helda")
To get a bug fix, or use a feature from the development version, you can install helda from this GitHub repository.
# install.packages("devtools") devtools::install_github("Redcart/helda")
This is a quick introduction to the lift curve function of the package:
library(helda) data_training <- titanic_training data_validation <- titanic_validation model_glm <- glm(formula = "Survived ~ Pclass + Sex + Age + SibSp + Fare + Embarked", data = data_training, family = binomial(link = "logit")) predictions <- predict(object = model_glm, newdata = data_validation, type = "response") plot <- lift_curve(predictions = predictions, true_labels = data_validation$Survived, positive_label = 1) plot
If you encounter a clear bug, please file a minimal reproducible example on the issues section of the repository.
Simon Corde
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