📊Friday AI Fact: What is a Confusion Matrix?
A confusion matrix is a table that helps you understand how well your classification model is performing.
Same time next Friday - new AI fact coming your way!
#ELOQUENCE #AI #MachineLearning #ConfusionMatrix
A confusion matrix is a table that helps you understand how well your classification model is performing.
Same time next Friday - new AI fact coming your way!
#ELOQUENCE #AI #MachineLearning #ConfusionMatrix
August 8, 2025 at 10:46 AM
📊Friday AI Fact: What is a Confusion Matrix?
A confusion matrix is a table that helps you understand how well your classification model is performing.
Same time next Friday - new AI fact coming your way!
#ELOQUENCE #AI #MachineLearning #ConfusionMatrix
A confusion matrix is a table that helps you understand how well your classification model is performing.
Same time next Friday - new AI fact coming your way!
#ELOQUENCE #AI #MachineLearning #ConfusionMatrix
#DataVisualization #IrisDataset #MLforBeginners #ConfusionMatrix
#MachineLearning #DataScience #ML #AI#Python #scikitlearn #Pandas #Seaborn #Matplotlib
#MachineLearning #DataScience #ML #AI#Python #scikitlearn #Pandas #Seaborn #Matplotlib
April 8, 2025 at 8:16 PM
The question is whether 70% sensitivity, 90% precision is good enough (maybe, for some applications). To play at home (in R): `tab <- matrix(c(229, 327-229, 27, 173-27), nrow=2, dimnames = list("Predicted" = c("Event", "No Event"), "Reference" = c("Event", "No Event")))
caret::confusionMatrix(tab)`
caret::confusionMatrix(tab)`
![To compare and validate these LLMs [sic] extracted interactions, we manually screened a subset of this text and extracted all interactions based on expert knowledge. The manual annotation of 500 paragraphs in the validation set identified 327 biological interactions. Our automated approach managed to identify 229 of these (true positives), while making 27 errors (false positives). This corresponds to an accuracy of 89.5% and a recall of 70.0%.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:eo2un6k4yqwliqszd7wgkhbi/bafkreid2ifdwaqni2x2n4cpyztdgn2c4rcdqntaccmbuqtpiaxivoxmjgq@jpeg)
January 28, 2025 at 9:55 PM
The question is whether 70% sensitivity, 90% precision is good enough (maybe, for some applications). To play at home (in R): `tab <- matrix(c(229, 327-229, 27, 173-27), nrow=2, dimnames = list("Predicted" = c("Event", "No Event"), "Reference" = c("Event", "No Event")))
caret::confusionMatrix(tab)`
caret::confusionMatrix(tab)`
#Confusion can be a great resource for learning... something like #StatisticalVariance #Uncertainty and #UnknownTerritory to guide #ScientificDiscovery
#SharedFacts
#SharedFacts
December 8, 2024 at 4:04 AM
#ConfusionMatrix #MeasurementScience
Fundamental for thinking about #PublicHealth #PopulationScience #ClinicalDecisions #DiagnosticError #PolicyAndPractice
#Sensitivity #Specificity #Classification
Fundamental for thinking about #PublicHealth #PopulationScience #ClinicalDecisions #DiagnosticError #PolicyAndPractice
#Sensitivity #Specificity #Classification
December 11, 2024 at 12:20 PM
December 11, 2024 at 12:20 PM