How to Build Accurate Curves and Handle Messy Data with Robust Estimation
Data scientists often struggle with spurious data points that distort polynomial curves and splines. This guide explores robust estimation techniques to help analysts build accurate, reliable curves despite noisy datasets. Mastering these methods ensures better predictive modeling and prevents outliers from skewing critical business or scientific insights.
Source: Towards Data Science