Blog
Mert Özen's blog posts on software development, backend, frontend, and DevOps.
Regression and Its Metrics: Linear Regression, Gradient Boosting, MAE, RMSE and R²
Regression predicts a number; choosing the right metric matters as much as choosing the algorithm, because MAE measures average deviation, RMSE punishes large errors and R² measures explained variance. We write gradient descent by hand in C#, compare SDCA, FastTree, FastForest and LightGBM on Bookstore's delivery time, and show with real output how 21 lost parcels nearly triple RMSE, how residual analysis works and how two model families behave outside the training range.
Your First ML.NET Model: MLContext, IDataView, Pipelines and Predicting Delivery Time
An ML.NET model is a chain of estimators built on MLContext that learns from training data with Fit; the result is a single transformer you can save and load. For Bookstore's delivery time we cover the input class, the lazy IDataView, the trap of empty cells being read as 0 by default, the first SDCA regression model, single predictions with PredictionEngine, saving the model as a .zip, and float/double, column name and thread-safety errors, all with real output.