Employee datasets often include a wide range of variables—demographics, performance metrics, engagement scores, and more. Analyzing all these variables without discernment can lead to noisy, overfit models that lack clarity and fail to deliver actionable outcomes. Feature selection is the process of identifying and retaining only the most relevant variables for your analysis, ensuring that your results are both accurate and impactful.

This article delves into the importance of feature selection in people analytics, explores key techniques for selecting the right variables, and offers practical guidance on applying these methods in HR contexts.

Why Feature Selection Matters in People Analytics

HR datasets can be vast and complex, with variables often correlated or redundant. Using all available variables in an analysis may lead to several issues:

• Overfitting Models: Including irrelevant or redundant variables can cause models to fit noise rather than patterns, reducing generalizability to new data.
• Increased Complexity: Larger models are harder to interpret, making it difficult to communicate insights to non-technical stakeholders.
• Slower Computation: More variables mean higher computational demands, which can slow down analysis, especially with large datasets.
• Diminished Insights: Irrelevant features dilute the impact of truly meaningful variables, making it harder to pinpoint actionable drivers of HR outcomes.

By selecting the most relevant features, people analysts can reduce noise, improve model performance, and focus attention on the factors that matter most.

Steps for Effective Feature Selection

Feature selection typically involves three key steps:

• Understand the Problem Context: Define the purpose of your analysis (e.g., predicting turnover, understanding engagement drivers).
• Assess Feature Relevance: Evaluate which variables are most relevant to the target outcome.
• Refine the Dataset: Retain only the variables that add value and remove those that don’t.

Let’s explore some of the techniques that make this process efficient and effective.

Techniques for Feature Selection in People Analytics

Domain Knowledge and Business Understanding

Before applying statistical or machine learning techniques, start with domain expertise. HR professionals and analysts should collaborate to identify variables that are likely to influence the target outcome based on their experience.

• Example: When analyzing turnover, tenure, engagement scores, and performance ratings might naturally come to mind as critical variables, whereas features like office seating arrangement might be less relevant.
• Why it matters: Domain knowledge provides a foundational filter, helping analysts focus on variables that align with organizational goals.

Correlation Analysis

Correlation analysis measures the linear relationship between each independent variable and the dependent variable. Variables with higher correlations are often more relevant to the target outcome.

• How to Use: Compute the correlation matrix to identify strong relationships between predictors and the outcome.
• Practical Consideration: Be cautious of multicollinearity (high correlation between predictors). For example, both engagement scores and manager satisfaction might correlate with performance but may also overlap in what they measure.

Example: In a retention model, if tenure has a correlation of -0.65 with turnover likelihood, it suggests that longer tenure is strongly associated with lower turnover rates.

Feature Engineering/Derived Variables

Feature selection often goes hand-in-hand with feature engineering, where new variables are derived from existing ones to capture more meaningful relationships.

• Example: Rather than using the raw "start date" field, you can create a derived variable like "tenure" (e.g., the number of months or years an employee has been with the organization). Tenure is often a critical factor in people analytics models, helping to uncover trends in turnover, performance, or engagement.
• Why it Matters: Thoughtful feature engineering can significantly enhance model performance by introducing variables that better align with real-world phenomena.

Principal Component Analysis

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated variables into uncorrelated components, retaining only the components that explain the most variance in the data.

• How to Use: Apply PCA to reduce high-dimensional datasets, especially when dealing with multicollinearity.
• Why it’s Relevant: PCA simplifies datasets without losing much information, but it sacrifices interpretability since components are combinations of original variables.

Example: In an employee engagement survey with 30 questions, PCA might reduce the dataset to 3–5 components, representing themes like “Leadership Trust” or “Work-Life Balance.”

Regularization Methods (Lasso and Ridge Regression)

Regularization techniques add a penalty to model complexity, effectively shrinking the coefficients of less important variables toward zero. This automatically selects the most impactful features.

• Lasso Regression: Encourages sparsity by completely excluding variables with minimal impact.
• Ridge Regression: Retains all variables but reduces the influence of less impactful ones.

Example: In a turnover prediction model, Lasso regression might exclude variables like “office floor number” while retaining key predictors like engagement and performance ratings.

Recursive Feature Elimination

Recursive Feature Elimination (RFE) is a machine learning-based technique that iteratively removes the least important features, refits the model, and ranks variable importance.

• How to Use: Apply RFE using algorithms like linear regression, decision trees, or support vector machines. The process ranks features based on their contribution to model accuracy.
• Practical Application: Use RFE to select the top 5–10 variables that maximize predictive accuracy.

Example: RFE might identify training hours, engagement scores, and tenure as the top predictors of performance ratings, discarding less impactful variables like commuting distance.

Balancing Simplicity and Accuracy

Feature selection is as much about interpretability as it is about accuracy. For HR stakeholders, simpler models with fewer variables are often more actionable and easier to explain. Here are a few strategies for balancing simplicity and accuracy:

• Prioritize Variables with Clear Interpretability: Choose features that are easy to explain to non-technical audiences (e.g., tenure or engagement scores).
• Limit the Number of Features: Avoid overwhelming models with excessive variables. Aim for a manageable set of predictors that capture the key drivers of the outcome.
• Regularly Reassess Feature Relevance: As workforce dynamics change, ensure that selected features remain relevant over time.

Conclusion

Feature selection is both a technical and strategic process. By choosing the right variables, people analysts can build models that are not only accurate but also actionable and aligned with organizational goals.

At DataSkillUp, we empower people analysts to master feature selection techniques and other foundational skills for impactful HR analytics. If you’re ready to take your HR career to the next level, connect with us for personalized coaching and training.

Book a 60-minute discovery call to learn how we can help you achieve your People Analytics goals here.

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