Evaluating Information Retrieval

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 1 of 4

Information Retrieval Basics: Document Frequency and Precision

Introduces information retrieval basics, emphasizing document frequency and precision in evaluating retrieval quality.

Information Retrieval Basics: Document Length and Normalization

Explores document length, normalization, bias compensation, and retrieval model evaluation in information retrieval.

Evaluation Protocols

Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.

Performance Evaluation: Bootstrap and Performance Metrics

Explores machine learning model evaluation using leave-one-out, bootstrap, and performance metrics like recall and precision.

Performance Criteria: Confusion Matrix, Recall, Precision, Accuracy

Explores performance criteria in supervised learning, emphasizing precision, recall, and specificity in model evaluation.

Information Retrieval: Basics and Techniques

Introduces the basics of Information Retrieval, covering indexing, weighting schemes, cosine similarity, and query evaluation.

Evaluation in NLP

Delves into NLP evaluation, covering gold standards, precision, recall, and statistical significance.

Probabilistic Retrieval Models

Covers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.

Quantifying Performance: Misclassification and F-Measure

Covers quantifying performance through true positives, false negatives, and false positives in machine learning.

The Riesz-Kakutani Theorem

Explores the construction of measures, emphasizing positive functionals and their connection to the Riesz-Kakutani Theorem.