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The goal of ensModelVis is to display model fits for multiple models and their ensembles.
You can install the development version of ensModelVis from GitHub with:
# install.packages("devtools")
devtools::install_github("domijan/ensModelVis")This is a basic example:
library(ensModelVis)
data(iris)
if (require("MASS")) {
lda.model <- lda(Species ~ ., data = iris)
lda.pred <- predict(lda.model)
}
#> Loading required package: MASS
if (require("ranger")) {
ranger.model <- ranger(Species ~ ., data = iris, mtry = 1)
ranger.pred <- predict(ranger.model, iris)
ranger.model2 <-
ranger(Species ~ .,
data = iris,
mtry = 4,
num.trees = 10)
ranger.pred2 <- predict(ranger.model2, iris)
}
#> Loading required package: ranger
plot_ensemble(
iris$Species,
data.frame(
LDA = lda.pred$class,
RF = ranger.pred$predictions,
RF2 = ranger.pred2$predictions
)
)
plot_ensemble(
iris$Species,
data.frame(
LDA = lda.pred$class,
RF = ranger.pred$predictions,
RF2 = ranger.pred2$predictions
),
incorrect = TRUE
)
if (require("ranger")) {
ranger.model <- ranger(Species ~ ., data = iris, mtry = 1, probability = TRUE)
ranger.prob <- predict(ranger.model, iris)
ranger.model2 <-
ranger(Species ~ .,
data = iris,
mtry = 4,
num.trees = 10,
probability = TRUE)
ranger.prob2 <- predict(ranger.model2, iris)
}
plot_ensemble(
iris$Species,
data.frame(LDA = lda.pred$class,
RF = ranger.pred$predictions,
RF2 = ranger.pred2$predictions),
tibble_prob = data.frame(
LDA = apply(lda.pred$posterior, 1, max),
RF = apply(ranger.prob$predictions, 1, max),
RF2 = apply(ranger.prob2$predictions, 1, max)
)
)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.