Performance Criteria: Confusion Matrix, Recall, Precision, Accuracy

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

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

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

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

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

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

Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.

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

Explains Sensitivity, Specificity, ROC curve, and Precision in diagnostic tests.

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

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

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

Explores statistical hypothesis testing, error types, thresholding, and multiple comparisons in GLM.

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

Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.

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

Explores Jaccard similarity, minhashing, and locality-sensitive hashing for data summarization.

Explores sources of unfairness in machine learning, the importance of fairness metrics, and evaluating model predictions using various fairness metrics.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.

Explores discrete choice and machine learning as complementary methodologies, discussing supervised learning, model advantages, pitfalls, aggregation bias, probabilistic classification, and panel data.