profit_thresholds: Find optimal threshold for churn prediction (class)

In modelimpact: Functions to Assess the Business Impact of Churn Prediction Models

Description

Finds the optimal threshold (from a business perspective) for classifying churners.

Usage

profit_thresholds(
  x,
  var_cost = 0,
  prob_accept = 1,
  tp_val = 0,
  fp_val = 0,
  tn_val = 0,
  fn_val = 0,
  prob_col = NA,
  truth_col = NA
)

Arguments

x	A data frame containing predicted probabilities of a target event and the actual outcome/class.
var_cost	Variable cost (e.g. of a campaign offer)
prob_accept	Probability of offer being accepted. Defaults to 1.
tp_val	The average value of a True Positive. 'var_cost' is automatically subtracted.
fp_val	The average cost of a False Positive. 'var_cost' is automatically subtracted.
tn_val	The average value of a True Negative.
fn_val	The average cost of a False Negative.
prob_col	The unquoted name of the column with probabilities of the event of interest.
truth_col	The unquoted name of the column with the actual outcome/class. Possible values are 'Yes' and 'No'. #' @return A data frame with the following columns: threshold = prediction thresholds payoff = calculated profit for each threshold

Examples

profit_thresholds(predictions,
   var_cost    = 100,
   prob_accept = .8,
   tp_val      = 2000,
   fp_val      = 0,
   tn_val      = 0,
   fn_val      = -2000,
   prob_col    = Yes,
   truth_col   = Churn)

modelimpact documentation built on May 6, 2021, 9:06 a.m.