How can redundant features affect a machine learning model?

Redundant features are input variables that contain the same or very similar information as other features in the dataset.

They may not add much new predictive value, but they can still increase model complexity.

Simple example

Suppose a dataset contains:

height_in_cm
height_in_meters

These two features carry essentially the same information in different units.

Keeping both usually adds little useful signal.

They can increase model complexity

Redundant features increase the number of inputs the model must process.

This can increase:

• training time
• memory usage
• prediction cost
• difficulty of interpreting the model

They can cause instability in some models

When two numerical features are strongly correlated, models such as linear regression may struggle to determine how much importance should be assigned to each one.

Small changes in the training data can then produce large changes in individual coefficient values.

This problem is related to multicollinearity, which is covered later in this section.

They can make feature importance harder to interpret

If several features contain nearly the same information, their importance may be split across them.

For example, instead of one feature appearing clearly important, several similar features may each receive part of the importance.

This can make explanations less straightforward.

They do not always reduce predictive accuracy

Some models, especially tree-based models, can tolerate redundant features reasonably well.

However, redundant features can still add unnecessary computation and make interpretation more difficult.

How can redundant features be handled?

Common approaches include:

• removing duplicate columns
• checking strong correlations between numerical features
• using domain knowledge to keep the most meaningful representation
• using feature-selection methods
• applying regularization when appropriate

Feature removal should still be validated because two similar features may behave differently in certain conditions.

In short, redundant features repeat information that is already available elsewhere. They can increase computation, complicate interpretation, and make some models unstable without necessarily adding useful predictive signal.