Statistic methods to compute the average of a list of metric results

I have a Machine Learning model that fits and predicts many time series at once, so, for each time series I have a metric result, for example, MAE. What I need is to generate an unique value for that metric for the whole model, not just for the individual predictions.

The metric results are a list, so they look like this:

metric_results = [0.2, 2.5, 4.5, 0.9, 1.6]

Possibilities tried for my data:

Mean: that doesn't look the best way to compute that, as the results don't represent the quality of the model as a whole, because as there are some zeroed values, the mean goes down, even though I know the model ain't performing that great.

Median: even worse results than mean.

Geometric mean: cannot be used because there are 0 values.

Harmonic mean: same problem as geometric mean.

So, is there a better way to compute the average of a list of these values?

• Could you please explain more about the task you’re trying to accomplish and why taking the mean or median of a collection of measurements makes for a poor summary of those measurements?
– Dave
Nov 5, 2022 at 15:43