gradient boosting for defect detection

**Gradient Boosting for Defect Detection** is the **application of gradient boosted tree models (XGBoost, LightGBM, CatBoost) to identify and classify wafer defects** — sequentially building trees that focus on the hardest-to-classify examples for superior detection accuracy. **How Does Gradient Boosting Work?** - **Sequential**: Each new tree corrects the errors of the previous ensemble. - **Gradient**: Fits trees to the negative gradient of the loss function (residuals). - **Regularization**: Learning rate, max depth, and L1/L2 penalties prevent overfitting. - **XGBoost**: The dominant implementation, with efficient handling of sparse data and missing values. **Why It Matters** - **Best Tabular Performance**: Gradient boosting consistently wins Kaggle competitions and industrial benchmarks on tabular data. - **Defect Classification**: Classifies defect types from SEM images, wafer maps, or process data. - **Class Imbalance**: Handles the severe class imbalance common in defect data (rare defects vs. many good samples). **Gradient Boosting** is **the premier ML algorithm for structured fab data** — sequentially correcting errors for the best defect detection accuracy on tabular process data.