Feature Engineering: How to Improve Model Performance
✅ What is Feature Engineering?
Feature engineering is the process of selecting, modifying, or creating new features from raw data to increase the predictive power of machine learning models.
🎯 Goals of Feature Engineering
Improve model accuracy
Reduce overfitting
Enhance model interpretability
Handle missing data, non-linearity, and domain-specific challenges
🔧 Common Feature Engineering Techniques
1. Creating New Features
Combining features: e.g., BMI = weight / height²
Ratios and differences: e.g., income_to_expense_ratio = income / expenses
Date/time features: Extract day, month, weekday, hour, season, etc.
Text features: Extract word counts, sentiment scores, keywords, etc.
Geographical features: Distance between coordinates, regions, etc.
2. Handling Categorical Variables
One-Hot Encoding: Converts categories to binary columns.
Label Encoding: Assigns an integer to each category.
Target Encoding: Replaces category with mean target value (watch for leakage).
Frequency Encoding: Uses the count of category occurrence.
3. Polynomial Features / Interactions
Add interaction terms or polynomial combinations (e.g., x1 * x2, x1²).
Useful for linear models to capture non-linearity.
4. Binning / Bucketing
Convert continuous variables into discrete intervals (e.g., age groups).
Helps decision tree models and handles outliers.
5. Log and Power Transformations
Used to reduce skewness in numerical features.
Example: log(x + 1) or sqrt(x)
6. Group-Based Aggregations
Aggregate statistics (mean, sum, count) by group/category.
Example: Average purchase amount per customer.
7. Dealing with Time Series
Lag features: Previous time step values.
Rolling statistics: Moving average, rolling std.
Time since last event: Useful in behavior modeling.
🧠 How Feature Engineering Improves Model Performance
Benefit How It Helps
Adds domain knowledge Embeds real-world insights into data
Captures non-linearities Especially useful in linear models
Improves feature-target link Enhances correlation with the target
Simplifies the model Helps models learn faster and generalize better
🛠 Example (Python using pandas)
python
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import pandas as pd
import numpy as np
# Example dataset
df = pd.DataFrame({
'income': [4000, 6000, 8000],
'expenses': [2000, 2500, 3000],
'dob': ['1990-01-01', '1985-05-12', '1970-07-30']
})
# New Feature: Income to Expense Ratio
df['income_expense_ratio'] = df['income'] / df['expenses']
# New Feature: Age from Date of Birth
df['dob'] = pd.to_datetime(df['dob'])
df['age'] = 2025 - df['dob'].dt.year
print(df)
🔄 When to Use Feature Engineering
Before model training: To extract and transform raw data.
During exploratory data analysis (EDA): To uncover useful patterns.
Iteratively: Revisit features based on model results and error analysis.
🚩 Tips for Effective Feature Engineering
Use domain knowledge: Features based on context often outperform automated ones.
Avoid data leakage: Don't use future or target-based info in training features.
Visualize features: Use plots to understand distributions and relationships.
Test with cross-validation: Measure performance impact of new features.
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