EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification

Overfitting detection for Gradient Boosting — no validation set required

This project builds and optimizes a model on a dataset using Ridge regression and polynomial features. Model accuracy is enhanced through regularization and polynomial transformations. Grid search and cross-validation are used to find the best parameters, and the model's performance is evaluated.