Machine Learning

A practical diagnosis map for underfitting, overfitting, model complexity, and generalization.

Why one split is fragile, how K-fold works, and when cross-validation can mislead in NLP.

How traditional ML features connect to embeddings, neural networks, and modern NLP systems.

How to tune hyperparameters without confusing search effort with scientific evidence.

Accuracy is easy to understand, but often wrong for imbalanced, ranked, or cost-sensitive tasks.

Model selection is the disciplined process of choosing among models without fooling yourself.