fn_error: Computes error measures between observed and predicted values

In scorecardModelUtils: Credit Scorecard Modelling Utils

Description

The function takes the input dataframe with observed and predicted columns and computes mean absolute error, mean squared error and root mean squared error terms.

Usage

fn_error(base, observed_col, predicted_col)

Arguments

base	input dataframe
observed_col	column / field name of the observed event
predicted_col	column / field name of the predicted event

Value

An object of class "fn_error" is a list containing the following components:

mean_abs_error	mean absolute error between observed and predicted value
mean_sq_error	mean squared error between observed and predicted value
root_mean_sq_error	root mean squared error between observed and predicted value

Author(s)

Arya Poddar <[e-mail]>

Examples

data <- iris
data$Species <- as.character(data$Species)
suppressWarnings(RNGversion('3.5.0'))
set.seed(11)
data$Y <- sample(0:1,size=nrow(data),replace=TRUE)
data$Y_pred <- sample(0:1,size=nrow(data),replace=TRUE)
fn_error_list <- fn_error(base = data,observed_col = "Y",predicted_col = "Y_pred")
fn_error_list$mean_abs_error
fn_error_list$mean_sq_error
fn_error_list$root_mean_sq_error

scorecardModelUtils documentation built on May 2, 2019, 9:59 a.m.