In the world of people analytics, it’s easy to get swept up in the excitement of using complex predictive models like machine learning or deep learning. However, when it comes to solving practical HR problems, simpler models—like linear regression or logistic regression—are often more effective and easier to implement.
Why Simpler Models Work in HR
HR datasets are often smaller and more structured compared to other business areas like marketing or finance. This makes simpler models more suitable because they:
- Require fewer data points: Many advanced models need large datasets to function accurately, but simpler models can still perform well with smaller HR datasets.
- Are easier to interpret: HR stakeholders, like managers or HR business partners, need to understand why the model is making certain predictions. Simpler models like linear regression provide clear, explainable outputs that allow you to show which factors (e.g., engagement, tenure) are driving predictions.
- Provide actionable insights: Simple models often focus on the key variables that impact outcomes like turnover or promotion likelihood. For example, logistic regression can predict whether an employee is likely to leave based on factors like engagement score, tenure, and recent performance ratings.
When to Use Simple Models
- Turnover Predictions: Logistic regression can easily model the likelihood of an employee leaving the company. It’s straightforward to explain which factors most strongly influence the decision.
- Compensation Analysis: Linear regression can help model salary distributions based on factors like years of experience, education level, and job role.
- Engagement vs. Performance: Using linear regression, you can see how engagement scores correlate with performance ratings, helping to identify the most engaged and productive employees.
Benefits of Simpler Models
- Ease of Use: Simple models are faster to build, test, and deploy. You don’t need advanced coding skills to implement them, making them more accessible for HR analysts.
- Faster Time-to-Insight: Simple models like linear and logistic regression allow HR teams to quickly predict outcomes like turnover or compensation trends, using existing tools like Excel or R, saving time on complex model building.
- Data Flexibility: HR data is often small and structured. Simple models handle limited datasets well, offering meaningful predictions without requiring large-scale resources.
- Ease of Interpretation: Simple models are easy to explain to non-technical stakeholders, ensuring that HR leaders can understand and act on the insights quickly, without getting lost in technical jargon.
In HR analytics, simpler models often provide the right balance between usability, interpretability, and accuracy, making them the go-to choice for many practical HR challenges.
How to Get Started with Simple Predictive Models
If you’re new to HR analytics or want to improve your data skills, simple predictive models are a great starting point.
- Learn the Basics: Familiarize yourself with linear and logistic regression. Use tools like Excel, R, or Python to run basic models.
- Collect and Clean Data: Ensure your data is organized and consistent for the model you run.
- Start Small: Apply linear regression to predict salary or use logistic regression to predict turnover. Practice with small projects to build confidence.
- Interpret & Communicate: Focus on explaining how each variable impacts the prediction and what actionable insights can be drawn. This fosters trust in the analysis.
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