Evaluation Protocols

Related lectures (32)

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

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

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

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

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

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Discusses the evaluation of binary classifiers, including recall, sensitivity, specificity, ROC curves, and performance measures.

Explains the evaluation of information retrieval models, including recall, precision, F-Measure, and the precision/recall tradeoff.