Why Your ML Models Are Quietly Failing

Picture this: You deploy a machine learning model that predicts customer conversions with 85% accuracy. Six months later, you discover it’s performing at 65% accuracy, and you have no idea when the decline started or what caused it.

This scenario plays …

Building on our previous exploration of tree-based models (https://dev.to/afrologicinsect/tree-based-models-for-alzheimers-disease-classification-a-tidymodels-approach-136h), let’s dive into ensemble methods and see how R makes it incredibly easy to build, deploy, and interact with …

Initially published on May 27, 2025 GAMs (Generalized Additive Models) handle non-linearity in model development through their flexible approach. The “additive” in the model implies that the response variable can be modeled as a sum of smooth functions of predictor variables, allowing …