'True positive rate' presentation slideshows

Solving Classification Problems for Symptom Validity Tests with Mixed Groups Validation

Solving Classification Problems for Symptom Validity Tests with Mixed Groups Validation. Richard Frederick, Ph.D., ABPP (Forensic) US Medical Center for Federal Prisoners Springfield, Missouri. I am not a neuropsychologist. My view of brain. Your view of brain. My board certifications:.

2.13k views • 181 slides

Solving Classification Problems for Symptom Validity Tests with Mixed Groups Validation

Solving Classification Problems for Symptom Validity Tests with Mixed Groups Validation. Richard Frederick, Ph.D., ABPP (Forensic) US Medical Center for Federal Prisoners Springfield, Missouri. I am not a neuropsychologist. My view of brain. Your view of brain. My board certifications:.

2.05k views • 181 slides

Marine Geospatial Ecology Tools Open Source Geoprocessing for Marine Ecology

Marine Geospatial Ecology Tools Open Source Geoprocessing for Marine Ecology. Jason Roberts, Ben Best, Dan Dunn, Eric Treml, Pat Halpin Duke University Marine Geospatial Ecology Lab 4-Mar-2009. Talk outline. Overview of MGET Quick tour of the MGET tool collection

602 views • 40 slides

Evaluating Classifiers

412 views • 21 slides

IMDB Improved (Random name that just stuck)

IMDB Improved (Random name that just stuck). Joshua Boren, Douglas Hitchcock, Calvin Kern, Ryan Norton. Recommenders. What is a recommender? How does a recommender learn? What applications are there for recommenders?. Steer Clear!.

254 views • 12 slides

Supplemental Figure 1. ROC plot of CD177 expression as a predictor of GI irAE

Supplemental Figure 1. ROC plot of CD177 expression as a predictor of GI irAE. 1.0. 0.8. 0.6. True positive rate. 0.4. 0.2. 0.0. 0.2. 0.4. 0.6. 0.8. 1.0. 0.0. False positive rate.

121 views • 1 slides

True positive rate

True positive rate. False positive rate.

181 views • 1 slides

Artificial Intelligence Research Laboratory Bioinformatics and Computational Biology Program

ISMB 2006. Artificial Intelligence Research Laboratory Bioinformatics and Computational Biology Program Computational Intelligence, Learning, and Discovery Program Department of Computer Science. On the Quality of Motifs for Protein Phosphorylation Site Prediction.

133 views • 1 slides