The Mean Absolute Error Calculator is a tool used to calculate the average absolute error between the predicted value and the actual value for a given set of data. It is a measure of accuracy for a predictive model and is calculated by taking the sum of absolute errors and dividing it by the number of data points. MAE is a useful indicator of how well a model is performing and can be used to compare different models.
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In statistics, the mean absolute error (MAE) is a way to measure the accuracy of a given model. It is calculated as:
MAE = (1/n) * Σ|yi – xi|
where:
- yi: The observed value for the ith observation
- xi: The predicted value for the ith observation
- n: The total number of observations
This calculator finds the mean absolute error for a given model.
Simply enter a list of observed values and a list of predicted values in the boxes below, then click the “Calculate” button:
Observed values:
Predicted values:
Mean Absolute Error = 2.42857
function calc() {
//get input data
var x = document.getElementById(‘x’).value.split(‘,’).map(Number);
var y = document.getElementById(‘y’).value.split(‘,’).map(Number);
//check that both lists are equal length
if (x.length – y.length == 0) {
document.getElementById(‘error_msg’).innerHTML = ”;
//calculate RMSE
let error = 0
for (let i = 0; i < x.length; i++) {
error += Math.abs(x[i] – y[i])
}
var mae = error / x.length;
document.getElementById('mae').innerHTML = mae.toFixed(5);
}
//output error message if both lists are not equal
else {
document.getElementById('error_msg').innerHTML = 'The two lists must be of equal length.';
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} //end calc function