What are best practices for handling categorical variables in ML models?

Introduction

In many real-world datasets handled in data science courses, you will face categorical variables—features whose values are labels or groups (e.g. “red/blue/green”, “city names”, “education level”). But most machine learning models require numeric inputs. Thus, one of the key challenges in feature engineering is: how to convert categorical variables into representations that ML models can use, without introducing bias, overfitting, or inefficiency.

In this article we present best practices, backed by statistics and literature, tailored for students learning data science. We weave in the idea of Quality Thought (i.e. thinking critically about your transformations) and explain how our courses can support you in mastering these techniques.

Why careful handling of categorical variables matters

A 2024 survey of encoding methods found that in published code bases, label encoding was used in 17 instances, dummy (one-hot) encoding in 16, etc., but no single method consistently dominates across datasets and models.
If you naively one-hot encode a categorical feature with 1,000 unique values, you add 1,000 extra binary columns, which may overwhelm models (curse of dimensionality) and make overfitting easier.
Encoding improperly (e.g. using label encoding for a nominal feature with no order) may mislead models into assuming artificial ordinal relationships.

Thus, a Quality Thought approach means carefully examining each categorical feature, its cardinality, frequency distribution, relationship to the target variable, and then choosing or adapting encoding techniques.

Best practices & techniques

1. Understand the type: nominal vs ordinal

Nominal: no intrinsic ordering (e.g. “Color”, “City”)
Ordinal: natural order (e.g. “Low / Medium / High”, education levels)
For ordinal features, you may safely map to integers (e.g. Low = 1, Medium = 2, High = 3). But even then, think: is the distance between “Low” and “Medium” equal to “Medium” to “High”? Always apply Quality Thought and possibly transform further.

2. Choose encoding carefully

Key practice: For each categorical variable, try multiple encoding methods and compare model validation metrics (e.g. cross-validated accuracy, AUC, RMSE) rather than settling on one method arbitrarily.

3. Handle rare levels / unseen categories

Combine rare categories under a new level “Other” to reduce sparsity.
Use smoothing, backoff, or fallback encoding for categories appearing in validation/test but unseen during training.
For hashing or fixed binning, new categories will map to some bucket.

4. Prevent overfitting

When using supervised encodings (target, mean, or weight-of-evidence), always apply regularization / smoothing or k-fold/leave-one-out encoding to prevent leaking target information.
Use cross-validation to evaluate your categorical encoding choices.
Drop columns that add little predictive value.

5. Document and reflect (Quality Thought)

Always log your transformations: which categories were merged, encoding formulas used, hyperparameters, etc.
After training, inspect which encoded features contributed most (e.g. by feature importance).
Reflect: did your encoding make sense, or did it “over-engineer”? That reflective step is part of Quality Thought.

How we help Educational Students via our Data Science course

In our Data Science Course, we adopt the “Quality Thought” philosophy—teaching students not just how to encode categorical variables, but why and when. Our course offers:

Hands-on labs: where students try different encodings (one-hot, target, embeddings) on real datasets and compare results.
Guided critical thinking sessions: where students learn to ask questions like “Is this categorical variable meaningful?”, “What’s its distribution?”, “Could rare levels be merged?”
Project work: students build end-to-end ML models, iterating encoding strategies, documenting decisions, comparing metrics.
Support & feedback: our instructors review student encoding choices and highlight potential pitfalls (overfitting, leakage).

This helps educational students internalize Quality Thought—making student work not just technically correct but conceptually sound.

Conclusion

Handling categorical variables is a foundational skill in data science. By applying a disciplined, reflective approach—what we call Quality Thought—students can choose encodings that balance expressiveness, interpretability, and generalization. Try, compare, document, and iterate. With the support of our Data Science Course, students become confident in transforming categorical data optimally. Are you ready to practice encoding techniques hands-on with curated datasets and guided feedback?

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