Too Many Buttons Nobody Needs

Opening Scene

An overzealous apprentice, eager to be thorough, adds buttons everywhere on a jacket — three sets doing essentially the same fastening job, each slightly redundant with the others. It doesn’t make the jacket function better. If anything, it makes the garment more confusing to actually use, harder to clean, and no more useful than a single well-placed set would have been. More fasteners didn’t mean more function; it just meant more redundancy dressed up as thoroughness.

That’s exactly the trap of loading a model up with features that are highly redundant with each other — more columns doesn’t automatically mean more genuine signal.

In Plain English

Redundant features, often called multicollinearity when they’re numeric and strongly correlated, are features that carry largely the same information as another feature already in the dataset. Having both doesn’t typically help a model learn more; it can actually make some models harder to interpret, slower to train, and occasionally less stable, since the model has to somehow split credit between features that are really saying the same thing.

The Old Way

Before “multicollinearity” had a formal statistical name, redundancy just quietly wasted effort without anyone naming the pattern:

A form asking the same question three different ways — no more information gathered, just more effort to fill out and more room for inconsistent answers.
A report tracking five metrics that all just measure the same underlying thing — more numbers, not more insight.
A closet full of near-identical garments — technically more options, not meaningfully more variety.

In each case, apparent thoroughness masked genuine redundancy — more of something without more of what actually mattered.

What’s Changing (and Why AI Is the Reason)

Tooling can now automatically detect highly correlated feature pairs at scale, flagging redundancy across hundreds of features far faster than a person manually comparing them one pair at a time.
Some modern modeling techniques are naturally more tolerant of redundant features than older ones were, reducing — though not eliminating — how much this needs active, manual pruning for every project.
As feature sets have grown far larger through automated feature generation (a theme from earlier in this series), the practical volume of redundancy has grown too, making systematic detection more necessary than it used to be, not less.

The Metaphor, Fully Extended

Tailor Shop	Redundant Feature Concept
Three redundant sets of fasteners on one jacket	Multiple features carrying largely the same information
A garment that’s harder to use despite more components	A model made harder to interpret by redundant features
Choosing one well-placed set of fasteners instead	Selecting a smaller set of genuinely distinct features
An apprentice mistaking more fasteners for more function	Mistaking more features for more genuine signal
Reviewing a garment’s components for genuine redundancy	Systematically checking a feature set for high correlation
A streamlined design using exactly what’s needed	A pruned, non-redundant feature set

For Beginners: What to Actually Do

Check for highly correlated features before finalizing a feature set — a simple correlation check catches an enormous amount of avoidable redundancy.
Don’t assume more features automatically means a better model; a smaller, genuinely distinct feature set often performs just as well and is easier to work with.
When two features seem redundant, investigate which one better represents the underlying concept before deciding which to keep.

For Practitioners and Leaders: The Deeper Layer

Build automated redundancy checks into standard feature engineering workflow, especially for projects using automated feature generation that can produce large, sprawling feature sets.
Recognize that redundancy tolerance varies by model type; know which of your team’s typical approaches are genuinely sensitive to this and which are more robust to it.
A leaner, non-redundant feature set is generally easier to maintain, explain, and debug over the life of a model — treat that as a real, ongoing benefit, not just a one-time cleanup task.

Quick Recap

Redundant or highly correlated features carry largely the same information, adding little genuine signal despite adding more columns.
This mirrors familiar patterns of apparent-but-hollow thoroughness — extra questions, extra metrics, extra near-identical options.
Automated correlation checks now make redundancy detection fast and systematic, even across large feature sets.
A leaner, genuinely distinct feature set is usually easier to interpret, maintain, and trust than a bloated one.

Where This Fits in the Series

Article 7 covered features that quietly stop reflecting reality over time; this article covered features that were never adding much distinct value in the first place. Article 9 looks directly at the process of deciding which features actually earn their place.