Tagged "evaluation"
Cross-validation explained: k-fold, stratified and grouped
Cross-validation gives a more reliable performance estimate than one split by rotating the validation fold. Learn k-fold, stratified, group and time-series variants.
Machine learning methodsIntermediate3 minData leakage in machine learning: how good scores lie
Data leakage lets outside information slip into training, inflating scores that collapse in production. Learn the common types and how to prevent them.
Machine learning methodsIntermediate3 minHow to evaluate a RAG system: retrieval and answer quality
Measure a RAG system in two layers: did retrieval find the right passages, and is the answer faithful to them? Learn recall@k, MRR, faithfulness and a practical test set.
Retrieval-augmented generationIntermediate3 minPrecision, recall and F1 score explained with an example
Accuracy can be misleading on imbalanced data. Learn the confusion matrix, precision, recall, F1 and thresholds through a worked phishing-detection example.
Machine learning methodsBeginner3 minTrain, validation and test sets explained
Why machine learning data is split three ways, what each split is for, how to split correctly with scikit-learn, and the mistakes that make a test score meaningless.
Machine learning methodsBeginner3 minWhy long context windows still miss things
A million-token context window does not mean a model uses every token well. Learn about the lost-in-the-middle effect, attention cost, and how to test it yourself.
LLM foundationsIntermediate3 min