To Fit or Not to Fit Data to a Model

What if Shakespeare was a data scientist? Today's big data necessitates - Let the data define the model.

By Bruce Ratner,

To fit or not to fit data to a model - that is the question:

Whether 'tis nobler in the mind to suffer

The slings and arrows of outrageously using

The statistical regression paradigm of

Fitting data to a pre-specified(!) model, conceived and tested

Within the small-data setting of the day, 206 years ago,

Or to take arms against a sea of troubles

And, by opposing, move aside fitting data to a model.

Today's big data necessitates - Let the data define the model.

Fitting big data to a pre-specified small-framed model

Produces a skewed model with

Doubt interpretability and questionable results.

When we have shuffled off the expected coil,

There's the respect of the GenIQ Model,

A machine-learning alternative regression model

To the statistical regression model.

GenIQ is an assumption-free, free-form model that

Maximizes the cum lift statistic, equally, the decile table.

This was originally published at

Bruce RatnerBruce Ratner, Ph.D.
, The Significant Statistician™, is President and Founder of DM STAT-1 Consulting, and the author of the best-selling book Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data.