Sniffing Out Errors
insideBIGDATA, November 26th, 2019
November 26, 2019,
Volume 260, Issue 4

As seasoned analysts will know; it can be difficult to identify when to draw a line under your Predictive Modelling, accept its performance as sufficient for your purposes and move on to deployment

"Analysts and Data Scientists will be familiar with examining residual plots for their models and looking for outlier errors that may indicate that the model's underlying assumptions have been broken, or that some of the data points might be extreme outliers that cause grave problems when trying to build a model on the whole data set.

But while examining residual plots is great from a qualitative point of view, as data natives, we should always be looking for quantitative methods for describing, classifying and understanding these errors.

What we need is a statistical analysis, that fulfills our desire to quantitatively understand the weaknesses in our models. One simple practice which meets this need and can help the indecisive data-practitioner to direct and allocate their limited time is Error Analysis..."

Read More ...

Keywords:

     
    Other articles in the IT - AI section of Volume 260, Issue 4:

    See all archived articles in the IT - AI section.