Which machine learning algorithms usually do not require feature scaling?

Some machine learning algorithms usually do not require feature scaling because their predictions do not depend directly on distances or the numerical magnitude of features.

The most important examples are tree-based models.

Decision trees

A decision tree learns rules such as:

age < 35
income > 500000

If a feature is rescaled, the threshold changes accordingly, but the ordering of the values remains the same.

For example:

income > 500000

could become:

scaled_income > 0.62

The tree can still make essentially the same split.

Random forests

Random forests are collections of decision trees, so they usually do not need feature scaling either.

Features can often remain in their original units without one large-scale feature automatically dominating another.

Gradient-boosted decision trees

Tree-based boosting methods also usually do not require scaling.

Examples include:

• Gradient Boosting Trees
• XGBoost
• LightGBM
• CatBoost

These models learn threshold-based splits rather than relying on Euclidean distance between observations.

Why scaling matters less for tree models

Tree-based models mainly care about the ordering of feature values and where useful split points occur.

Multiplying a feature by a constant or converting it to another consistent scale usually preserves that ordering.

Scaling can still appear in the same pipeline

Even if the final model is tree-based, scaling may still be needed when another preprocessing step depends on scale.

For example, a pipeline that uses PCA, K-Means, or a distance-based transformation before the tree model may still require scaling.

Do not assume every non-tree model is scale-independent

Whether scaling matters depends on how an algorithm performs its calculations.

Distance-based, gradient-based, margin-based, and variance-based methods are often more sensitive to feature scale.

In short, decision trees, random forests, and tree-based gradient boosting models such as XGBoost, LightGBM, and CatBoost usually do not require feature scaling because their decisions are based mainly on feature ordering and split thresholds rather than feature magnitude.