What Is Feature Engineering in Machine Learning?

September 12, 2025

Feature engineering is the process of using domain knowledge to select, modify, or create new input features (variables, attributes) from raw data to improve the performance of machine learning models.

Why Feature Engineering Matters:

Most machine learning algorithms don't work well with raw data. Good features help the model learn patterns more effectively, often improving accuracy, robustness, and generalizability.

🔧 Key Steps in Feature Engineering:

1. Feature Creation

Creating new features that better represent the underlying problem:

Example: From a “timestamp” feature, create “hour of day”, “day of week”, or “is_weekend”.

Combining features: e.g., BMI = weight / height².

2. Feature Transformation

Transforming features to improve model compatibility:

Normalization / Standardization (scaling data).

Log Transform for skewed data.

Binning (e.g., converting continuous values into categorical buckets).

3. Feature Selection

Choosing the most relevant features and removing irrelevant or redundant ones:

Using statistical tests, correlation, or model-based methods (e.g., Lasso, tree-based feature importance).

4. Encoding Categorical Variables

Converting categorical data into numerical form:

One-Hot Encoding

Label Encoding

Target Encoding

5. Handling Missing Data

Dealing with incomplete values:

Imputation (mean, median, mode, predictive models).

Creating a “missing” indicator.

🧠 Real-World Example:

For a house price prediction model:

Raw data: YearBuilt, LotArea, Neighborhood, GarageType.

Feature engineering might involve:

HouseAge = CurrentYear - YearBuilt

HasGarage = 1 if GarageType else 0

One-hot encoding for Neighborhood

Log-transforming LotArea due to skewness

💡 Summary

Feature engineering is crucial in the ML pipeline—often more impactful than the choice of algorithm. It’s both an art and science, requiring creativity and understanding of the data and domain.

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