Explanatory Modeling vs Predictive Modeling

Developers should learn explanatory modeling when working on projects that require understanding why phenomena occur, such as in scientific research, A/B testing analysis, or business intelligence to inform decision-making meets developers should learn predictive modeling when working on projects that require forecasting, classification, or regression tasks, such as in finance for stock price prediction, healthcare for disease diagnosis, or e-commerce for recommendation systems. Here's our take.

Explanatory Modeling

Nice Pick

Developers should learn explanatory modeling when working on projects that require understanding why phenomena occur, such as in scientific research, A/B testing analysis, or business intelligence to inform decision-making

Pros

• It is essential in fields like economics, social sciences, and healthcare, where interpreting model coefficients and assessing causal effects is critical for drawing valid conclusions and driving policy or strategy
• Related to: statistical-analysis, regression-analysis

Cons

• Specific tradeoffs depend on your use case

Predictive Modeling

Developers should learn predictive modeling when working on projects that require forecasting, classification, or regression tasks, such as in finance for stock price prediction, healthcare for disease diagnosis, or e-commerce for recommendation systems

Pros

• It enables data-driven insights and automation of predictive tasks, enhancing applications with intelligent features like fraud detection or personalized content delivery
• Related to: machine-learning, statistics

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Explanatory Modeling is a methodology while Predictive Modeling is a concept. We picked Explanatory Modeling based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Explanatory Modeling wins

Based on overall popularity. Explanatory Modeling is more widely used, but Predictive Modeling excels in its own space.