Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
This post is about using custom error metrics in crossval, a tool offering generic functions for the cross-validation of Statistical/Machine Learning models. More information about cross-validation of regression models using crossval can be found in this post, or this other one. The default error measure for regression in crossval is Root Mean Squared Error (RMSE). Here, I’ll show you how to obtain two other error metrics:
- Mean Absolute Percentage Error (MAPE)
- Mean Absolute Error (MAE)
The same principles can be extended to any other error metric of your choice.
Installation of crossval
From Github, in R console, let’s start by installing crossval:
devtools::install_github("thierrymoudiki/crossval")
Cross-validation demo
Simulated dataset are used for this demo. With 100 examples, and 5 explanatory variables:
# dataset creation
set.seed(123)
n <- 100 ; p <- 5
X <- matrix(rnorm(n * p), n, p)
y <- rnorm(n)
Define functions for calculating cross-validation error (MAPE and MAE):
- MAPE
# error measure 1: Mean Absolute Percentage Error - MAPE
eval_metric_mape <- function (preds, actual)
{
res <- mean(abs(preds/actual-1))
names(res) <- "MAPE"
return(res)
}
- MAE
# error measure 2: Mean Absolute Error - MAE
eval_metric_mae <- function (preds, actual)
{
res <- mean(abs(preds - actual))
names(res) <- "MAE"
return(res)
}
Linear model fitting, with RMSE, MAE and MAPE errors
X contains the explanatory variables.
y is the response.
k is the number of folds in k-fold cross-validation.
repeats is the number of repeats of the k-fold cross-validation procedure.
- Defaut - Root Mean Squared Error - RMSE
crossval::crossval_ml(x = X, y = y, k = 5, repeats = 3)
##
|
| | 0%
|
|============= | 20%
|
|========================== | 40%
|
|======================================= | 60%
|
|==================================================== | 80%
|
|=================================================================| 100%
## user system elapsed
## 0.149 0.005 0.163
## $folds
## repeat_1 repeat_2 repeat_3
## fold_1 0.8987732 0.9270326 0.7903096
## fold_2 0.8787553 0.8704522 1.2394063
## fold_3 1.0810407 0.7907543 1.3381991
## fold_4 1.0594537 1.1981031 0.7368007
## fold_5 0.7593157 0.8913229 0.7734180
##
## $mean
## [1] 0.9488758
##
## $sd
## [1] 0.1902999
##
## $median
## [1] 0.8913229
- Mean Absolute Percentage Error - MAPE
crossval::crossval_ml(x = X, y = y, k = 5, repeats = 3,
eval_metric = eval_metric_mape)
##
|
| | 0%
|
|============= | 20%
|
|========================== | 40%
|
|======================================= | 60%
|
|==================================================== | 80%
|
|=================================================================| 100%
## user system elapsed
## 0.117 0.003 0.127
## $folds
## repeat_1 repeat_2 repeat_3
## fold_1 1.486233 0.9517148 1.1181554
## fold_2 1.382454 1.1669799 1.0954839
## fold_3 1.267862 1.0583498 1.7768124
## fold_4 1.110386 1.1569593 1.3466701
## fold_5 1.242622 1.6604326 0.9615794
##
## $mean
## [1] 1.25218
##
## $sd
## [1] 0.2411539
##
## $median
## [1] 1.16698
- Mean Absolute Error - MAE
crossval::crossval_ml(x = X, y = y, k = 5, repeats = 3,
eval_metric = eval_metric_mae)
##
|
| | 0%
|
|============= | 20%
|
|========================== | 40%
|
|======================================= | 60%
|
|==================================================== | 80%
|
|=================================================================| 100%
## user system elapsed
## 0.118 0.003 0.133
## $folds
## repeat_1 repeat_2 repeat_3
## fold_1 0.7609698 0.6799802 0.6528781
## fold_2 0.7548409 0.7061494 0.9147533
## fold_3 0.8246641 0.5686014 1.0612401
## fold_4 0.7378648 0.9079500 0.5792025
## fold_5 0.6176459 0.7448324 0.6630864
##
## $mean
## [1] 0.7449773
##
## $sd
## [1] 0.1357212
##
## $median
## [1] 0.7378648
Note: I am currently looking for a gig. You can hire me on Malt or send me an email: thierry dot moudiki at pm dot me. I can do descriptive statistics, data preparation, feature engineering, model calibration, training and validation, and model outputs’ interpretation. I am fluent in Python, R, SQL, Microsoft Excel, Visual Basic (among others) and French. My résumé? Here!
Under License Creative Commons Attribution 4.0 International.
Comments powered by Talkyard.