# Feature Engineering (Data Science & ML)

> Categorical encoding, scaling, normalization, feature selection, feature creation, pipelines

- 22 interview questions
- Mid-Level
- [Interview Questions: Data Science & ML](https://sharpskill.dev/en/technologies/data-science/interview-questions.md)

## 1. Which encoding type should be used for a nominal categorical variable with few distinct categories (less than 10)?

**Answer**

One-Hot Encoding is ideal for nominal variables with few categories because it creates a binary column for each category without introducing artificial ordering. Unlike Label Encoding which assigns numbers (0, 1, 2...), One-Hot prevents the model from interpreting a non-existent ordinal relationship between categories.

## 2. What is the main difference between StandardScaler and MinMaxScaler?

**Answer**

StandardScaler centers data around 0 with a standard deviation of 1 (z-score), while MinMaxScaler normalizes data within a fixed range, usually [0, 1]. StandardScaler is less sensitive to outliers because it uses mean and standard deviation, whereas MinMaxScaler can be strongly affected by extreme values.

## 3. Which scaler should be preferred when data contains significant outliers?

**Answer**

RobustScaler uses median and interquartile range (IQR) instead of mean and standard deviation, making it robust to outliers. Extreme values do not significantly affect these statistics, unlike StandardScaler or MinMaxScaler which can be strongly biased by outliers.

## 19 more questions available

- What is Label Encoding and when is it appropriate to use it?
- What problem can Target Encoding cause and how to avoid it?

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## Other Data Science & ML interview topics

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