wlzpoly is a Python package that decomposes N-point wafer thickness measurements into M Zernike polynomial coefficients using LSQ or Ridge regression with LOOCV-tuned regularization. It ships with a reproducible three-stage demo (synthetic data, coefficient fitting, ground-truth verification), per-stage CLI, and a clean pure-Python API for semiconductor wafer-level process metrology.
This report analyzes why standard vectorization methods — statistical summary (mean/var/AUC), automatic feature extraction (tsfresh, catch22), convolutional representations (MiniRocket), and self-supervised embeddings (TS2Vec) — fail when the time series length T is very short, especially T ≤ 10. As an alternative, it argues that Raw Vectorization (Identity Mapping) is not just an easy fallback but…
1. What is One-Hot Encoding? One-hot encoding is the most fundamental technique for converting categorical variables into numerical vectors that machine learning models can process. Given N unique categories, each category is represented as an N-dimensional vector with a 1 at the position corresponding to that category and 0 elsewhere. Example: [‘Seoul’, ‘Busan’, ‘Daegu’] →…
1. Analysis of Small Positive Ranges (0 to 0.2) While a target range of 0 to 0.2 is mathematically valid, it presents several practical challenges in model training and optimization. 1.1 Training Speed and Convergence Issues 1.2 Loss Function Sensitivity 1.3 Compatibility with Activation Functions 2. Analysis of Negative Ranges (-1 to 0) Targeting a…
Delicate dandelion seed heads Random Seed Sensitivity in Feature Selection 1. Problem Statement Feature selection results can vary significantly depending on the random seed used during model training, data splitting, or stochastic optimization. This instability undermines reproducibility and trustworthiness, particularly in high-stakes domains such as healthcare and genomics where consistent feature identification is critical for…
