I remember a client with a big WooCommerce store, around 50k SKUs, who was convinced their custom recommendation engine was worthless. They were reading “Accuracy” at a 0.5 threshold, seeing 51%, and calling it a coin flip. They were ready to scrap months of work and go back to basic “Related Products.” I asked for a CSV of the raw scores and the actual conversion data, then said we should look at the Machine Learning AUC in Excel before deciding anything. The model was ranking properly. Their threshold was in the wrong place.
Most developers fall into the accuracy trap. If the model says 0.4 and the result is 1, they call it a failure, which only holds if you have arbitrarily decided 0.5 is the cutoff. I have done the same thing. Early in my career I burned a whole weekend tweaking gradient descent variants to fix what I thought was slow training, when the model was already capturing the ranking signal and I was judging it with the wrong metric. What you want to measure is how well the model ranks, not whether it clears one particular threshold.
How to calculate machine learning AUC in Excel
The ROC (Receiver Operating Characteristic) curve is a set of confusion matrices, one for every possible threshold. Plot the True Positive Rate (TPR) against the False Positive Rate (FPR) and you get a staircase. AUC is the area under that staircase, and a spreadsheet handles it without a Jupyter notebook. Start by sorting your data by model score, highest to lowest. Get the sort wrong and the area you calculate means nothing. This step-by-step ROC guide shows what the manual setup looks like.
Then calculate the cumulative TPR and FPR at each row. The threshold at any row is that row’s score: everything above it counts as a predicted positive, everything below as a predicted negative, so working down the list relaxes the threshold. Google explains the ROC sweep the same way. Each step of the staircase is a trapezoid, so take the average height (TPR) and multiply it by the width, which is the change in FPR.
/**
* Pseudo-logic for the Excel trapezoid calculation
* Column A: Actual Class (0 or 1)
* Column B: Model Score (Sorted DESC)
* Column C: Cumulative TPR
* Column D: Cumulative FPR
*/
// In Excel, the Area for a specific row would look like this:
// = (Current_TPR + Previous_TPR) / 2 * (Current_FPR - Previous_FPR)
function bbioon_calculate_auc_logic($data) {
$auc = 0;
foreach ($data as $index => $row) {
if ($index == 0) continue;
$delta_fpr = $row['fpr'] - $data[$index-1]['fpr'];
$avg_tpr = ($row['tpr'] + $data[$index-1]['tpr']) / 2;
$auc += ($avg_tpr * $delta_fpr);
}
return $auc;
}What the Machine Learning AUC in Excel approach shows you is ranking quality. Pick one positive example and one negative example at random, and the AUC is the probability that the model gives the positive one the higher score. That holds up much better than accuracy when your data is imbalanced. It matters on any complex WooCommerce integration. A model with an AUC of 0.9 is doing its job even when its accuracy at 0.5 looks terrible.
What to remember
- Accuracy describes one threshold; AUC describes the whole model.
- Sorting by score comes first in any Machine Learning AUC in Excel calculation.
- The trapezoidal rule is the safest way to sum the area in a spreadsheet.
- Check the ranking before you start rewriting loss functions.
Data science in production is messy. If your models look good in theory and fall over in the actual shop, the numbers you are watching may be the problem. For more on the probabilistic reading of AUC, DataCamp has a solid breakdown of the math behind the curve.
If you are tired of debugging someone else’s models and want a recommendation engine that converts, drop me a line. I have probably seen the problem before, and I have fixed it before.
