How Do I Determine Which Features to Engineer for My Specific Machine Learning Model?

Building a great machine learning model is like baking the perfect cake. The right ingredients matter — not everything in your pantry belongs. This guide shows you how to identify and craft features that truly make a difference. Stop guessing. Start engineering success.

Understanding Features in Machine Learning

A feature is a measurable property or characteristic of the data you’re working with. For example:

  • In a house price prediction model, features could be square footage, number of bedrooms, or neighborhood.
  • In a customer churn model, features might include last login date, number of purchases, or subscription length.

The key challenge? Determining which features will have the most significant impact on your model’s performance.

Step 1: Understand the Problem and Domain

Before jumping into data:

1. Define Your Objective:

Ask: What are you trying to predict or analyze? Is it house prices, customer churn, or loan defaults?‍

2. Collaborate with Domain Experts:

Domain knowledge can help uncover relationships that aren't obvious in the raw data. For instance:

  • A real estate agent might tell you that houses near schools are more valuable.
  • A marketer might highlight that frequent complaints predict customer churn.
Example:

In a credit scoring model, domain knowledge might reveal that recent missed payments carry more weight than those from years ago.

Step 2: Explore Your Data

Dive into the raw data and identify potential features. Here's how:

a. Identify Key Variables

  • Look for columns that logically relate to the outcome.
  • Example: In predicting customer churn, "Days Since Last Login" is more relevant than "Account Creation Year."

b. Use Data Visualizations

Visualizations can reveal trends and relationships between features and your target variable.

  • Scatter plots for continuous data (e.g., house size vs. price).
  • Box plots for categorical data (e.g., customer segments vs. churn rate).

c. Compute Correlations

Check how features are correlated with the target variable.

  • Use Pearson correlation for linear relationships.
  • Use Spearman correlation for monotonic relationships.
Example Correlation Table:
Feature Correlation with Target
Number of Purchases 0.75
Days Since Last Login -0.65
Customer Segment 0.30

Step 3: Engineer Features Relevant to Your Model

Feature engineering is where the magic happens. Here’s how to do it effectively:

a. Create Domain-Specific Features

  • Combine or transform existing columns into more meaningful ones.
  • Example: Instead of using "Login Timestamps," calculate "Days Since Last Login."

b. Handle Categorical Variables

  • Use label encoding for ordinal data (e.g., education levels).
  • Use one-hot encoding for nominal data (e.g., product categories).
Example: One-Hot Encoding
Product Category Category_Food Category_Clothing Category_Electronics
Food 1 0 0
Electronics 0 0 1

c. Engineer Interaction Features

  • Combine features to capture interactions.
  • Example: Instead of "Number of Bedrooms" and "House Size," create "Bedrooms per 1000 Sq Ft."

d. Extract Temporal Features

  • From dates or timestamps, create features like "Day of the Week," "Month," or "Quarter."
  • Example: A retail sales model might benefit from identifying weekends or holiday seasons.

e. Use Statistical Aggregates

  • For grouped data, calculate averages, sums, or standard deviations.
  • Example: In customer segmentation, calculate "Avg Spend per Visit" or "Total Spend in Last 6 Months."

Step 4: Select the Most Relevant Features

Once you’ve engineered a bunch of features, it’s time to pick the best ones. Here’s how:

a. Use Feature Importance

  • Algorithms like decision trees or random forests can rank feature importance.
  • Example: A random forest might show that "Last Purchase Date" is the most predictive feature in a churn model.

b. Perform Recursive Feature Elimination (RFE)

  • Gradually remove less important features and check model performance.

c. Use Statistical Tests

  • For numeric features, use ANOVA or Chi-Square tests to assess relevance.

Real-World Example: Predicting Employee Attrition

Let’s say you’re building a model to predict whether employees will leave a company. Here’s how feature engineering might look:

Raw Data Column Engineered Feature Why It Matters
Hire Date Tenure (in months) Tenure influences attrition.
Last Promotion Date Time Since Last Promotion Shows career progression.
Monthly Salary Salary Band Groups data for better analysis.
Work Hours per Week Overtime (Yes/No) Excessive hours signal burnout.

Step 5: Tailor Features to Your Model Type

Different models benefit from different types of features:

Linear Models (e.g., Logistic Regression):

  • Focus on features that show linear relationships.
  • Avoid redundant or highly correlated features.

Tree-Based Models (e.g., Random Forest, XGBoost):

  • These models handle non-linear relationships and don’t require scaled data.
  • Focus on meaningful interaction features.

Neural Networks:

  • Ensure features are scaled and normalized.
  • Consider dimensionality reduction techniques like PCA.

Step 6: Iterate and Refine

Feature engineering isn’t a one-and-done process. After training your model:

  1. Check which features are underperforming.
  2. Revisit your data to find more insightful features.

Takeaway

Determining the right features to engineer is both an art and a science. It requires understanding your problem, diving into your data, and iteratively experimenting with features. Remember, the better your features, the better your model — and the more meaningful your insights.

‍

Cohorte Team

December 11, 2024