modelplotr: modelplotr: Plots to Evaluate the Business Performance of...

In modelplotr: Plots to Evaluate the Business Performance of Predictive Models

Description

Plots to evaluate the business performance of predictive models in R. A number of widely used plots to assess the quality of a predictive model from a business perspective can easily be created. Using these plots, it can be shown how implementation of the model will impact business targets like response on a campaign or return on investment. It's very easy to apply modelplotr to predictive models that are developed in caret, mlr, h2o or keras. For other models, even those built outside of R, an instruction is included. The modelplotr package provides three categories of important functions: datapreparation, plot parameterization and plotting.

Datapreparation functions

The datapreparation functions are:

prepare_scores_and_ntiles

Function that builds a dataframe that contains actuals and predictions on the target variable for each dataset in datasets and each model in models. As inputs, it takes dataframes to score and model objects created with caret, mlr, h2o or keras. Specifically for keras models, built with keras_model_sequential() or with the keras functional API, there is the prepare_scores_and_ntiles_keras function. To use modelplotr on top of models created otherwise, even models built outside r, see aggregate_over_ntiles

plotting_scope

Function that creates a dataframe in the required format for all modelplotr plots, relevant to the selected scope of evaluation. Each record in this dataframe represents a unique combination of datasets, models, target classes and ntiles. As an input, plotting_scope can handle both a dataframe created with aggregate_over_ntiles as well as a dataframe created with prepare_scores_and_ntiles (or with prepare_scores_and_ntiles_keras or created otherwise, with similar layout).

aggregate_over_ntiles

Function that aggregates the output of prepare_scores_and_ntiles to create a dataframe with aggregated actuals and predictions. Each record in this dataframe represents a unique combination of datasets, models, target classes and ntiles. In most cases, you do not need to use function since the plotting_scope function will call this function automatically.

Parameterization functions

Most parameterization functions are internal functions. However, one is available for customization:

customize_plot_text

Function that returns a list that contains all textual elements for all plots that modelplotr can create. By changing the elements in this list - simply by overwriting values - and then including this list with the custom_plot_text parameter in plot functions, plot texts can easily be customized to meet your (language) preferences

Plotting functions

The plotting functions are:

plot_cumgains

Generates the cumulative gains plot. This plot, often referred to as the gains chart, helps answering the question: When we apply the model and select the best X ntiles, what percentage of the actual target class observations can we expect to target?

plot_cumlift

Generates the cumulative lift plot, often referred to as lift plot or index plot, helps you answer the question: When we apply the model and select the best X ntiles, how many times better is that than using no model at all?

plot_response

Generates the response plot. It plots the percentage of target class observations per ntile. It can be used to answer the following business question: When we apply the model and select ntile X, what is the expected percentage of target class observations in that ntile?

plot_cumresponse

Generates the cumulative response plot. It plots the cumulative percentage of target class observations up until that ntile. It helps answering the question: When we apply the model and select up until ntile X, what is the expected percentage of target class observations in the selection?

plot_multiplot

Generates a canvas with all four evaluation plots - cumulative gains, cumulative lift, response and cumulative response - combined on one canvas

plot_costsrevs

It plots the cumulative costs and revenues up until that ntile when the model is used for campaign selection. It can be used to answer the following business question: When we apply the model and select up until ntile X, what are the expected costs and revenues of the campaign?

plot_profit

Generates the Profit plot. It plots the cumulative profit up until that ntile when the model is used for campaign selection. It can be used to answer the following business question: When we apply the model and select up until ntile X, what is the expected profit of the campaign?

plot_roi

Generates the Return on Investment plot. It plots the cumulative revenues as a percentage of investments up until that ntile when the model is used for campaign selection. It can be used to answer the following business question: When we apply the model and select up until ntile X, what is the expected investment of the campaign?

Author(s)

Jurriaan Nagelkerke <[e-mail]> [aut, cre]

Pieter Marcus <[e-mail]> [aut]

See Also

vignette('modelplotr')

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

https://modelplot.github.io/ for our blog posts on using modelplotr

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=100)

# set scope for analysis (default: no comparison)
plot_input <- plotting_scope(prepared_input = scores_and_ntiles)
head(plot_input)

# ALL PLOTS, with defaults
plot_cumgains(data=plot_input)
plot_cumlift(data=plot_input)
plot_response(data=plot_input)
plot_cumresponse(data=plot_input)
plot_multiplot(data=plot_input)
# financial plots - these need some financial parameters
plot_costsrevs(data=plot_input,fixed_costs=1000,variable_costs_per_unit=10,profit_per_unit=50)
plot_profit(data=plot_input,fixed_costs=1000,variable_costs_per_unit=10,profit_per_unit=50)
plot_roi(data=plot_input,fixed_costs=1000,variable_costs_per_unit=10,profit_per_unit=50)

# CHANGING THE SCOPE OF ANALYSIS
# changing the scope - compare models:
plot_input <- plotting_scope(prepared_input = scores_and_ntiles,scope="compare_models")
plot_cumgains(data=plot_input)
# changing the scope - compare datasets:
plot_input <- plotting_scope(prepared_input = scores_and_ntiles,scope="compare_datasets")
plot_roi(data = plot_input,fixed_costs=1000,variable_costs_per_unit=10,profit_per_unit=50)
# changing the scope - compare target classes:
plot_input <- plotting_scope(prepared_input = scores_and_ntiles,scope="compare_targetclasses")
plot_response(data=plot_input)
# HIGHLIGHTING OPTIONS
plot_input <- plotting_scope(prepared_input = scores_and_ntiles,
                             scope = 'compare_datasets',select_model_label = 'random forest')
plot_cumgains(data=plot_input,highlight_ntile=20)
plot_cumlift(data=plot_input,highlight_ntile=20,highlight_how = 'plot')
plot_response(data=plot_input,highlight_ntile=20,highlight_how = 'text')
plot_cumresponse(data=plot_input,highlight_ntile=20,highlight_how = 'plot_text')
plot_costsrevs(data=plot_input,fixed_costs = 1000,variable_costs_per_unit = 10,
               profit_per_unit = 50,highlight_ntile='max_roi')
plot_profit(data=plot_input,fixed_costs = 1500,variable_costs_per_unit = 10,profit_per_unit = 50)
plot_roi(data=plot_input,fixed_costs = 1500,variable_costs_per_unit = 10,profit_per_unit = 50)

# OTHER PLOT CUSTOMIZATIONS
# customize line colors
plot_input <- plotting_scope(prepared_input = scores_and_ntiles,scope = 'compare_models')
plot_cumgains(data=plot_input,custom_line_colors = c('pink','navyblue'))
# customize all textual elements of plots
plot_input <- plotting_scope(prepared_input = scores_and_ntiles)
mytexts <- customize_plot_text(plot_input = plot_input)
mytexts$cumresponse$plottitle <- 'Expected conversion rate for Campaign XYZ'
mytexts$cumresponse$plotsubtitle <- 'proposed selection: best 15 percentiles according to our model'
mytexts$cumresponse$y_axis_label <- '% Conversion'
mytexts$cumresponse$x_axis_label <- 'percentiles (percentile = 1% of customers)'
mytexts$cumresponse$annotationtext <-
"Selecting up until the &NTL percentile with model &MDL has an expected conversion rate of &VALUE"
plot_cumresponse(data=plot_input,custom_plot_text = mytexts,highlight_ntile = 15)

## End(Not run)

modelplotr documentation built on Oct. 23, 2020, 8:20 p.m.