· Xiaojing Yang · Machine Learning
Cross-Validation for Model Evaluation
Why one split is fragile, how K-fold works, and when cross-validation can mislead in NLP.
Blog
Foundations and research notes on mathematics, statistics, machine learning, NLP, LLMs, model evaluation, and applied AI systems.
- · Xiaojing Yang · Statistics
Effect Size: Statistical Significance Is Not Enough
A statistically significant result can still be too small to matter in research or deployment.
- · Xiaojing Yang · Machine Learning
Feature Engineering vs Representation Learning
How traditional ML features connect to embeddings, neural networks, and modern NLP systems.
- · Xiaojing Yang · NLP and LLMs
Fine-Tuning Transformers
How pretrained language models are adapted to a task or domain with supervised data.
- · Xiaojing Yang · Machine Learning
Grid Search and Randomized Search
How to tune hyperparameters without confusing search effort with scientific evidence.
- · Xiaojing Yang · Statistics
Hypothesis Testing Without Rituals
Hypothesis testing is a way to discipline claims, not a ritual for producing p-values.