modelplotr (version 1.1.0)

plot_multiplot: Create plot with all four evaluation plots

Description

Generates a layout containing a number graphical elements, including title, subtitle and the four model evaluation plots: cumulative gains plot, lift plot, response plot and cumulative response plot.

Usage

plot_multiplot(
  data = plot_input,
  save_fig = FALSE,
  save_fig_filename = NA,
  custom_line_colors = NA,
  highlight_ntile = NA,
  custom_plot_text = NULL
)

Arguments

data

Dataframe. Dataframe needs to be created with plotting_scope or else meet required input format.

save_fig

Logical. Save plot to file? Default = FALSE. When set to TRUE, saved plot is optimized for 36x24cm.

save_fig_filename

String. Filename of saved plot. Default the plot is saved as tempdir()/plotname.png.

custom_line_colors

Vector of Strings. Specifying colors for the lines in the plot. When not specified, colors from the RColorBrewer palet "Set1" are used.

highlight_ntile

Integer. Specifying the ntile at which the plot is annotated and/or performances are highlighted.

custom_plot_text

List. List with customized textual elements for plot. Create a list with defaults by using customize_plot_text and override default values to customize.

Value

gtable, containing 6 grobs.

See Also

modelplotr for generic info on the package moddelplotr

vignette('modelplotr')

plotting_scope for details on the function plotting_scope that transforms a dataframe created with prepare_scores_and_ntiles or aggregate_over_ntiles to a dataframe in the required format for all modelplotr plots.

aggregate_over_ntiles for details on the function aggregate_over_ntiles that aggregates the output of prepare_scores_and_ntiles to create a dataframe with aggregated actuals and predictions. In most cases, you do not need to use it since the plotting_scope function will call this function automatically.

https://github.com/modelplot/modelplotr for details on the package

https://modelplot.github.io/ for our blog on the value of the model plots

Examples

# NOT RUN {
# load example data (Bank clients with/without a term deposit - see ?bank_td for details)
data("bank_td")
# prepare data for training model for binomial target has_td and train models
train_index =  sample(seq(1, nrow(bank_td)),size = 0.5*nrow(bank_td) ,replace = FALSE)
train = bank_td[train_index,c('has_td','duration','campaign','pdays','previous','euribor3m')]
test = bank_td[-train_index,c('has_td','duration','campaign','pdays','previous','euribor3m')]
#train models using caret... (or use mlr or H2o or keras ... see ?prepare_scores_and_ntiles)
# setting caret cross validation, here tuned for speed (not accuracy!)
fitControl <- caret::trainControl(method = "cv",number = 2,classProbs=TRUE)
# random forest using ranger package, here tuned for speed (not accuracy!)
rf = caret::train(has_td ~.,data = train, method = "ranger",trControl = fitControl,
                  tuneGrid = expand.grid(.mtry = 2,.splitrule = "gini",.min.node.size=10))
# mnl model using glmnet package
mnl = caret::train(has_td ~.,data = train, method = "glmnet",trControl = fitControl)
# load modelplotr
library(modelplotr)
# transform datasets and model objects to input for modelplotr
scores_and_ntiles <- prepare_scores_and_ntiles(datasets=list("train","test"),
                         dataset_labels = list("train data","test data"),
                         models = list("rf","mnl"),
                         model_labels = list("random forest","multinomial logit"),
                         target_column="has_td",
                         ntiles=10)
plot_input <- plotting_scope(prepared_input = scores_and_ntiles)
plot_multiplot(data=plot_input)
plot_multiplot(data=plot_input,highlight_ntile = 2)
# }