Explanatory Modeling vs Machine Learning

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 machine learning to build intelligent applications that can automate complex tasks, provide personalized user experiences, and extract insights from large datasets. 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

Machine Learning

Developers should learn Machine Learning to build intelligent applications that can automate complex tasks, provide personalized user experiences, and extract insights from large datasets

Pros

• It's essential for roles in data science, AI development, and any field requiring predictive analytics, such as finance, healthcare, or e-commerce
• Related to: artificial-intelligence, deep-learning

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Explanatory Modeling is a methodology while Machine Learning 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 Machine Learning excels in its own space.