# Custom errors for cross-validation using crossval::crossval_ml

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This post is about **using custom error measures** 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 measures:

- Mean Absolute Percentage Error (
**MAPE**) - Mean Absolute Error (
**MAE**)

The **same principles can be extended to any other error measure** 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!

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