Elasticsearch machine learning allows for the development of regression and classification models that can analyze complex data, and the introduction of feature importance in Elastic Stack 7.6 enhances the interpretability of these models. Through the use of data frame analytics, which employs decision trees to predict outcomes based on historical data, feature importance provides a locally-accurate linear model representation that helps users understand and verify predictions. In a practical example using the World Happiness Report, feature importance was applied to explore factors influencing happiness across different countries, indicating that healthy life expectancy and GDP per capita are significant predictors of happiness, with the former being more influential overall. The methodology uses the SHAP (SHapley Additive exPlanations) algorithm to assign values to features, thus enabling the interpretation of model predictions and the discovery of data relationships. This approach reveals that while wealth is a factor in happiness, other aspects like social connections and health play crucial roles, particularly in countries at the extremes of the wealth spectrum. Feature importance, as part of the Elastic Stack, offers a valuable tool for gaining insights into data and understanding model predictions, fostering a deeper comprehension of the factors that contribute to outcomes such as happiness.