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Results for "precision and recall"

  1. Article · Machine learning methods

    Precision, 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.

  2. Learning path · Learning path: AI

    Machine learning methods that hold up

    Data splits, overfitting, cross-validation, the right metrics and data leakage.

  3. Article · Retrieval-augmented generation

    How vector search works: similarity, top-k and hybrid search

    Vector search finds text by meaning instead of exact words. Learn cosine similarity, top-k retrieval, approximate nearest neighbour indexes, BM25 and hybrid ranking.

  4. Lab · Machine learning methods

    Measure overfitting with validation curves

    Train decision trees of increasing depth on a real dataset, plot training and validation scores, and find the point where the model starts memorising instead of learning.

  5. Article · Machine learning methods

    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.

  6. Article · Retrieval-augmented generation

    How 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.

  7. Learning path · Learning path: AI

    Build and evaluate RAG systems

    Chunking, retrieval, grounded prompts, evaluation and security for answering from your own documents.

  8. Article · Retrieval-augmented generation

    How to chunk documents for RAG

    Chunking decides what your RAG system can retrieve. Compare fixed-size, recursive, heading-aware and semantic chunking, and learn how to pick size and overlap.

  9. Article · LLM foundations

    Why 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.