ROC Curve смотреть последние обновления за сегодня на .
ROC (Receiver Operator Characteristic) graphs and AUC (the area under the curve), are useful for consolidating the information from a ton of confusion matrices into a single, easy to interpret graph. This video walks you through how to create and interpret ROC graphs step-by-step. We then show how the AUC can be used to compare classification methods and, lastly, we talk about what to do when your data isn't as warm and fuzzy as it should be. NOTE: This is the 2019.07.11 revision of a video published earlier. NOTE: This video assumes you already know about Confusion Matrices... 🤍 ...Sensitivity and Specificity... 🤍 ...and the example I work through is based on Logistic Regression, so it would help to understand the basics of that as well: 🤍 For a complete index of all the StatQuest videos, check out: 🤍 If you'd like to support StatQuest, please consider... Buying The StatQuest Illustrated Guide to Machine Learning!!! PDF - 🤍 Paperback - 🤍 Kindle eBook - 🤍 Patreon: 🤍 ...or... YouTube Membership: 🤍 ...a cool StatQuest t-shirt or sweatshirt: 🤍 ...buying one or two of my songs (or go large and get a whole album!) 🤍 ...or just donating to StatQuest! 🤍 Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: 🤍 0:00 Awesome song and introduction 0:48 Classifying samples with logistic regression 4:03 Creating a confusion matrices for different thresholds 7:12 ROC is an alternative to tons of confusion matrices 13:44 AUC to compare different models 14:28 False Positive Rate vs Precision 15:38 Summary of concepts Correction: 12:00 The confusion matrix should be TP = 3, FP = 2, FN = 1, TN = 2. The displayed matrix should be for the next point. #statquest #ROC #AUC
Onderzoek, Wetenschap, Geneeskunde, Epidemiologie, Methodologie, Onderwijs, Educatie, Klinische Wetenschap, Medische Wetenschap, Medisch Onderzoek, Instructievideo’s Tags: multiple testing, herhaalde meting, herhaald testen, p-waarde, onterecht positief, type I fout, fout-positief Relevant en juist onderzoek herkennen? Ga naar 🤍 en koop de syllabus van het NTvG 'Hoe lees ik wetenschappelijk onderzoek'. Of volg de gratis online leermodule ‘Medische informatie de baas’ (🤍ntvg.nl/qm) . Het NTvG helpt u kaf van koren te scheiden. Dat scheelt tijd.
If you have multiple diagnostic tests for the same disease, how can you know which one is better? And if you have to choose a certain point as a cutoff for your test, based on what you will do that? The answers to these questions seem to be really complicated, but trust ATP, it is not! In today's video we will discuss The ROC curve and its applications, and how you can employ it to better deal with diagnostic tests. Enjoy! Content: 0:15 - ROC curve introduction 0:55 - How ROC curve is built 1:58 - Sensitivity & specificity in the ROC curve 2:49 - ROC curve: screening & confirmatory tests (how to choose the optimal cutoff) 3:46 - How to compare between to diagnostic tests 4:25 - Accuracy 5:07 - How to calculate TP, TN, FP & FN using the ROC curve 5:41 - Summary Credits: - Arabic Subtitles: Tarek Arabi - Illustrations: Anas Idris - Script: Khaled Abdullah - Video Editing: Anas Idris & Mohamad S. Alabdaljabar - Voice Over: Khaled Abdullah For more content on biostats: Youtube.com/Khalemedic
The ROC curve is a very effective way to make decisions on your machine learning model based on how important is it to not allow false positives or false negatives. In this video we introduce the ROC curve with a simple example. Grokking Machine Learning Book: 🤍 40% discount promo code: serranoyt Machine Learning Testing and Error Metrics 🤍
Why ROC and AUC is needed | ROC curve analysis | ROC curve analysis in python #ROCcurve #machinelearning #datascience Hello , My name is Aman and I am a Data Scientist. All amazing data science courses at most affordable price here: 🤍 Topics for the video: Why ROC and AUC is needed ROC curve analysis ROC curve analysis in python ROC curve diagnostics test ROC curve analysis About Unfold Data science: This channel is to help people understand basics of data science through simple examples in easy way. Anybody without having prior knowledge of computer programming or statistics or machine learning and artificial intelligence can get an understanding of data science at high level through this channel. The videos uploaded will not be very technical in nature and hence it can be easily grasped by viewers from different background as well. Book recommendation for Data Science: Category 1 - Must Read For Every Data Scientist: The Elements of Statistical Learning by Trevor Hastie - 🤍 Python Data Science Handbook - 🤍 Business Statistics By Ken Black - 🤍 Hands-On Machine Learning with Scikit Learn, Keras, and TensorFlow by Aurelien Geron - 🤍 Ctaegory 2 - Overall Data Science: The Art of Data Science By Roger D. Peng - 🤍 Predictive Analytics By By Eric Siegel - 🤍 Data Science for Business By Foster Provost - 🤍 Category 3 - Statistics and Mathematics: Naked Statistics By Charles Wheelan - 🤍 Practical Statistics for Data Scientist By Peter Bruce - 🤍 Category 4 - Machine Learning: Introduction to machine learning by Andreas C Muller - 🤍 The Hundred Page Machine Learning Book by Andriy Burkov - 🤍 Category 5 - Programming: The Pragmatic Programmer by David Thomas - 🤍 Clean Code by Robert C. Martin - 🤍 My Studio Setup: My Camera : 🤍 My Mic : 🤍 My Tripod : 🤍 My Ring Light : 🤍 Join Facebook group : 🤍 Follow on medium : 🤍 Follow on quora: 🤍 Follow on twitter : 🤍unfoldds Get connected on LinkedIn : 🤍 Follow on Instagram : unfolddatascience Watch Introduction to Data Science full playlist here : 🤍 Watch python for data science playlist here: 🤍 Watch statistics and mathematics playlist here : 🤍 Watch End to End Implementation of a simple machine learning model in Python here: 🤍 Learn Ensemble Model, Bagging and Boosting here: 🤍 Build Career in Data Science Playlist: 🤍 Artificial Neural Network and Deep Learning Playlist: 🤍 Natural langugae Processing playlist: 🤍 Understanding and building recommendation system: 🤍 Access all my codes here: 🤍 Have a different question for me? Ask me here : 🤍 My Music: 🤍
This video demonstrates how to calculate and interpret a Receiver Operator Characteristic (ROC) Curve in SPSS. Evaluating sensitivity and specificity to inform selection of cutoff values is reviewed.
An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: 🤍 JOIN the "Data School Insiders" community and receive exclusive rewards: 🤍 RESOURCES: - Transcript and screenshots: 🤍 - Visualization: 🤍 - Research paper: 🤍 LET'S CONNECT! - Newsletter: 🤍 - Twitter: 🤍 - Facebook: 🤍 - LinkedIn: 🤍
ROC (Receiver Operator Characteristic) graphs and AUC (the area under the curve), are useful for consolidating the information from a ton of confusion matrices into a single, easy to interpret graph. This video walks you through how to create and interpret ROC graphs step-by-step. If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. You can find me on: GitHub - 🤍 Medium - 🤍 #auc #roc #machinelearning #python #deeplearning #datascience
This tutorial walks you through, step-by-step, how to draw ROC curves and calculate AUC in R. We start with basic ROC graph, learn how to extract thresholds for decision making, calculate AUC and partial AUC and how to layer multiple ROC curves on the same graph. You can get a copy of the code from the StatQuest GitHub, here: 🤍 NOTE: This StatQuest builds on the example in the original ROC and AUC StatQuest: 🤍 Also, if you're curious, here are some links to StatQuests about... ...Logistic Regression 🤍 ...and Random Forests... 🤍 For a complete index of all the StatQuest videos, check out: 🤍 If you'd like to support StatQuest, please consider... Buying The StatQuest Illustrated Guide to Machine Learning!!! PDF - 🤍 Paperback - 🤍 Kindle eBook - 🤍 Patreon: 🤍 ...or... YouTube Membership: 🤍 ...a cool StatQuest t-shirt or sweatshirt: 🤍 ...buying one or two of my songs (or go large and get a whole album!) 🤍 ...or just donating to StatQuest! 🤍 Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: 🤍 #statquest #ROC #AUC
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: 🤍 Instructor: Allison O'Hair Receiver Operator Characteristic (ROC) curves can help you decide which threshold value is the best depending. License: Creative Commons BY-NC-SA More information at 🤍 More courses at 🤍
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Use ROC curves to assess classification models. ROC curves plot the true positive rate vs. the false positive rate for different values of a threshold. This video walks through several examples that illustrate broadly what ROC curves are and why you’d use them. It also outlines interesting scenarios you may encounter when using ROC curves. Get started with MATLAB for Machine Learning with these interactive examples. You can run the examples right in your browser to see MATLAB in action: 🤍 - Learn more about MATLAB for machine learning: 🤍 - Machine Learning with MATLAB eBook: 🤍 - Get a Free Machine Learning Trial: 🤍 Get a free product Trial: 🤍 Learn more about MATLAB: 🤍 Learn more about Simulink: 🤍 See What's new in MATLAB and Simulink: 🤍 © 2019 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See 🤍mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Figures and Curves are common in biostats, and the ROC curve is quite a challenging one. Hopefully with this short video, you'll be able to understand it a lot better! Want to support me further? 👉 🤍 If you appreciate all the hard work that went into this video, please show it some love and drop me a like & comment! If you'd like to support me even that bit more, you can share this video with your friends and on social media! Also, subscribe and hit that notification 🔔 to never miss any of my future uploads! Sensitivity and Specificity: 🤍 📝 Content: 00:00 ROC Curve 📝 Notes/Errata: 1) None; please comment if you found an inconsistency and I'll be sure to add it here along with the time stamp! Social Media: 🐦 Twitter: Khalemedic 📸 Instagram: Khalemedic 🙋🏻♂️ Who am I? I'm Khaled, a final-year medical student, and I make videos on quite a few different topics within the scope of medicine such as reacting and breaking down medical shows, studying for exams, discussing important health issues, etc... I hope that my videos help you in any way, shape, or form and that you enjoy them regardless of your background. Medicine is quite the challenging topic, but I don't see why we can't enjoy it together! Disclaimer: The information and education material contained herein is meant to promote the general understanding of medical topics by healthcare professionals and related parties. Such information is not meant or intended to serve as a substitute for a healthcare professional's clinical training, experience, or judgment. For patient’s care or regarding your own health please consult your doctor or refer to the most recent evidence based practice.
#machinelearning#learningmonkey In this class, we discuss the ROC Curve for Binary Classification. For understanding the ROC Curve for Binary Classification we need to understand the Confusion matrix. The terms TPR and FPR are to be understood clearly. As we discussed in our previous class FPR decreasing TNR will increase. The problem with accuracy calculation in imbalanced data set. Even the model not concern about minority class points. The dumb models are taken granted because of the accuracy measure considered. ROC (Receiver Operating Characteristic) curve is one such technique which considers minority class into consideration. For generating the ROC Curve model has to predict the probability scores. Let's take an example of cancer prediction data set. 1 means the patient having cancer. 0 means the patient not having cancer. Suppose our model predicted a probability score of 0.96. The meaning of the above score is. 96 percent chance of our testing point to become 1. Let's take all the probability scores of our training data. Arrange in descending order of probability scores. Now take the highest probability value as a threshold value. The probability score above and equal are considered as 1 and below the threshold value as 0. Now using predicted and actual values calculate FPR and TPR values. Next, take the second-highest probability value as the threshold value. Find the prediction based on the threshold value as mentioned above. Again calculate FPR and TPR values based on predicted and actual values. Repeat this to all the probability scores. Now we get a bunch of FPR and TPR values. Plot those values taking FPR on X-axis and TPR on Y-axis. FPR and TPR values lie between 0 and 1. The area under the FPR and TPR values are 1. After plotting The FPR and TPR values we have to find the Area Under ROC Curve. This Area Under the ROC curve is taken as accuracy in imbalanced data sets. The more the area the accuracy of the model is more. why? Because we plotting FPR vs TPR. The less the FPR and the more the TPR. then we have a large area under the ROC curve. The less the FPR means the more the TNR. we discussed in the Confusion matrix. We are considering the model that takes a large area Under the ROC curve. So TNR is taken into consideration. The dumb models are not taken in this way of considering accuracy. Link for playlists: 🤍 Link for our website: 🤍 Follow us on Facebook 🤍 🤍 Follow us on Instagram 🤍 🤍 Follow us on Twitter 🤍 🤍 Mail us 🤍 learningmonkey01🤍gmail.com
#roccurve #rocspace #auc #machinelearning An ROC curve is obtained by plotting in the roc space the points fpr tpr obtained by assigning all possible values to the parameter or threshold in the classifier. AUC means area under the roc curve.
ROC is a probability curve and AUC represents the degree or measure of separability. #MachineLearning #ROC #AUC #ConvexHull Machine Learning 👉🤍 Artificial Intelligence 👉🤍 Cloud Computing 👉🤍 Wireless Technology 👉🤍 Data Mining 👉🤍 Simulation Modeling 👉🤍 Big Data 👉🤍 Blockchain 👉🤍 IOT 👉🤍 Follow me on Instagram 👉 🤍 Visit my Profile 👉 🤍 Support my work on Patreon 👉 🤍
In this Code Club, Pat shows how he would pool ROC curves so that you can directly assess a model's sensitivity for specificity. The area under the receiver operator characteristic (ROC) curve (AUC) is a useful metric of performance, but it isn't always the best way to assess performance since it looks over all possible specificities. The challenge is that with the mikropml framework we get one ROC curve per 80/20 training-testing split and we need to pool the curves to get a composite ROC curve. Even if you don't care about ROC curves, this episode is sure to have a lot of value for you including a little known R tip towards the end of the episode! Pat uses functions from the #mikropml R package and the #ggplot2 and #caret packages in #RStudio. The accompanying blog post can be found at 🤍 If you're interested in taking an upcoming 3 day R workshop, email me at riffomonas🤍gmail.com! R: 🤍 RStudio: 🤍 Raw data: 🤍 Workshops: 🤍 You can also find complete tutorials for learning R with the tidyverse using... Microbial ecology data: 🤍 General data: 🤍 0:00 Introduction 3:19 Calculating sensitivity and specificity for a continuous variable 11:47 Interpolating between specificity values 16:44 Generating ROC curve data for many splits 21:23 Plotting pooled ROC curves 26:11 Recap
In this video, I will show you how to plot the Receiver Operating Characteristic (ROC) curve in Python using the scikit-learn package. I will also you how to calculate the area under an ROC (AUROC) curve. In the tutorial, we will be comparing 2 classifiers via the ROC curve and the AUROC values. 🌟 Buy me a coffee: 🤍 📎CODE: 🤍 ⭕ Playlist: Check out our other videos in the following playlists. ✅ Data Science 101: 🤍 ✅ Data Science YouTuber Podcast: 🤍 ✅ Data Science Virtual Internship: 🤍 ✅ Bioinformatics: 🤍 ✅ Data Science Toolbox: 🤍 ✅ Streamlit (Web App in Python): 🤍 ✅ Shiny (Web App in R): 🤍 ✅ Google Colab Tips and Tricks: 🤍 ✅ Pandas Tips and Tricks: 🤍 ✅ Python Data Science Project: 🤍 ✅ R Data Science Project: 🤍 ⭕ Subscribe: If you're new here, it would mean the world to me if you would consider subscribing to this channel. ✅ Subscribe: 🤍 ⭕ Recommended Tools: Kite is a FREE AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite and I love it! ✅ Check out Kite: 🤍 ⭕ Recommended Books: ✅ Hands-On Machine Learning with Scikit-Learn : 🤍 ✅ Data Science from Scratch : 🤍 ✅ Python Data Science Handbook : 🤍 ✅ R for Data Science : 🤍 ✅ Artificial Intelligence: The Insights You Need from Harvard Business Review: 🤍 ✅ AI Superpowers: China, Silicon Valley, and the New World Order: 🤍 ⭕ Stock photos, graphics and videos used on this channel: ✅ 🤍 ⭕ Follow us: ✅ Medium: 🤍 ✅ FaceBook: 🤍 ✅ Website: 🤍 (Under construction) ✅ Twitter: 🤍 ✅ Instagram: 🤍 ✅ LinkedIn: 🤍 ✅ GitHub 1: 🤍 ✅ GitHub 2: 🤍 ⭕ Disclaimer: Recommended books and tools are affiliate links that gives me a portion of sales at no cost to you, which will contribute to the improvement of this channel's contents. #dataprofessor #PCA #clustering #cluster #principalcomponentanalysis #scikit #scikitlearn #sklearn #prediction #jupyternotebook #jupyter #googlecolab #colaboratory #notebook #machinelearning #datascienceproject #randomforest #decisiontree #svm #neuralnet #neuralnetwork #supportvectormachine #python #learnpython #pythonprogramming #datascience #datamining #bigdata #datascienceworkshop #dataminingworkshop #dataminingtutorial #datasciencetutorial #ai #artificialintelligence #tutorial #dataanalytics #dataanalysis #factor #principalcomponent #principalcomponents #pc #machinelearningmodel
#1. How to plot ROC Curve | Receiver Operating Characteristic Curve | Area Under Curve | False Positive Rate vs True Positive Rate by Mahesh Huddar #ROC #ROCCurve #TPRvsFPR Confusion Matrix Solved Example: 🤍 The following concepts are discussed: How to plot ROC Curve, Receiver Operating Characteristic Curve, Area Under Curve, False Positive Rate vs True Positive Rate, roc curve in machine learning, roc curve in Matlab, roc curve solved example, roc curve solving roc curve example 1. Blog / Website: 🤍 2. Like Facebook Page: 🤍 3. Follow us on Instagram: 🤍 4. Like, Share, Subscribe, and Don't forget to press the bell ICON for regular updates
Receiver operating curves or ROC curves are often used to compare diagnostic tests and to predict the accuracy of tests. Area under the curve can be calculated from ROC curves. We will learn how to interpret ROC curves and apply our knowledge to solve a few USMLE style questions on ROC curves. - Deep dives into the challenging topics covered by the USMLE Step 1 exam, created by Sujata Arecanteparamb, M.D. - founder of GraceUSMLE and author of Achievable's USMLE Step 1 course. To view our course and try it for free, visit 🤍
In this video, we will describe the difference between Area Under the Curve (AUC) and receiver Operating Characteristic (ROC) Curves. ROC Curve is a metric that assesses the model ability to distinguish between binary (0 or 1) classes. The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The true-positive rate is also known as sensitivity, recall or probability of detection in machine learning. The false-positive rate is also known as the probability of false alarm and can be calculated as (1 − specificity). Points above the diagonal line represent good classification (better than random) The model performance improves if it becomes skewed towards the upper left corner. The light blue area represents the area Under the Curve of the Receiver Operating Characteristic (AUROC). The diagonal dashed red line represents the ROC curve of a random predictor with AUROC of 0.5. If ROC AUC = 1, this means that we have a perfect classifier Higher the AUC, the better the model is at predicting 0s as 0s and 1s as 1s. I hope you guys enjoyed this video. Please subscribe to my channel for more videos and see you next week. Thanks and Happy Learning! #ROC #AUC #ReceiverOperatingCharacteristic #AreaUnderCurve
This video demonstrates how to obtain receiver operating characteristic (ROC) curves using the statistical software program SPSS SPSS can be used to determine ROC curves for various types of data.
In a typical diagnostic test analysis, an individual is given a score with the intent that the score will be useful in predicting whether the individual has or does not have the condition of interest. Based on a (hopefully large) number of individuals for which the score and condition is known, researchers may use ROC curve analysis to determine the ability of the score to classify or predict the condition. The analysis may also be used to determine the optimal cutoff value (or optimal decision threshold). For a given cutoff value, a positive or negative diagnosis is made for each unit by comparing the measurement to the cutoff value. If the measurement is less (or greater, as the case may be) than the cutoff, the predicted condition is negative. Otherwise, the predicted condition is positive. However, the predicted condition doesn’t necessarily match the true condition of the individual. There are four possible outcomes: true positive, true negative, false positive, false negative. For a given cutoff value, each individual falls into only one of the four outcomes. When all of the individuals are assigned to the four outcomes for a given cutoff, a count for each outcome is produced. Various rates can be used to describe a classification table. Some of the more commonly used rates are the true positive rate, or sensitivity, the true negative rate, or specificity, the false positive rate, the positive predictive value, the proportion correctly classified, or accuracy, and the sensitivity plus specificity. Each of the rates are calculated for a given table, based on a single cutoff value. An ROC curve plots the true positive rate (or sensitivity) against the false positive rate for all possible cutoff values. The ROC curve gives a visual representation of how well the diagnostic test performs across all false positive rates. Better diagnostic tests are those with ROC curves that reach closer to the top left corner, since they better maintain a true positive rate. The diagonal line serves as a reference line since it is the ROC curve of a diagnostic test that randomly classifies the condition. The area under the ROC curve provides a numeric representation of the overall performance of the diagnostic test. NCSS also provides the capability to produce a smooth estimate of the ROC curve, called the bi-Normal estimation ROC curve. To produce an ROC curve in NCSS, two columns of data are needed: a condition column, representing the known condition of each individual, and a score column, giving the score for each individual for the diagnostic test. The ‘One ROC Curve and Cutoff Analysis’ procedure can be opened from the menus. In this example, the Condition Variable is assigned the Condition column, and a positive condition is assigned the value of one. The Score is the Criterion Variable. Since, in this example, higher scores are more likely to imply a positive condition, the Criterion Direction is set to ‘Higher values indicate a Positive Condition’. We’ll leave checked the set of standard reports. The Run button is pressed to generate the report. The first several numeric tables show a variety of summary statistics for each of the cutoff values. Each statistic is defined in the Definitions section below the report. The Area Under Curve Analysis report gives a statistical test comparing the area under the curve to the value 0.5. The small P-value indicates a significant difference from 0.5. The report also gives the 95% confidence interval for the estimated area under the curve. Finally, the ROC curve itself is shown. It is seen to be moderately away from the 45 degree line and seems to indicate a decent separation from random classification. If we wish to determine the optimal cutoff value for this diagnostic test, two common indices to consider are the accuracy, which is the proportion correctly classified, and the sensitivity plus specificity, which is the true positive rate plus the true negative rate. Both of these indices point to seven as the optimal cutoff value, or optimal decision threshold.
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- ROC (Receiver Operator Characteristic) graphs, AUC (Area under the curve) are important evaluation metrics for calculating the performance of classification models. -ROC Curves summarize the trade-off between the true positive rate and false-positive rate (FPR) for a predictive model using different probability thresholds. Precision-Recall curves summarize the trade-off between the true positive rate and the positive predictive value for a predictive model using different probability thresholds. -ROC curves should be used when there are roughly equal numbers of observations for each class (balanced data). Precision-Recall curves should be used when there is a moderate to large class imbalance. -
In this video, I've shown how to plot ROC and compute AUC using scikit learn library. #scikitlearn #python #machinelearning Support me if you can ❤️ 🤍 🤍 For more videos please subscribe - 🤍 Source code - 🤍 Previous video on ROC-AUC - 🤍 Facebook - 🤍 Instagram - 🤍 Twitter - 🤍
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Contact us +989128186605 | GraphPad.ir🤍Gmail.com | 🤍 See the full tutorial on the GraphPad site 🤍 You want to choose a cutoff value that separates 'normal' from 'abnormal' test results. To help make the decision, plot the tradeoff of sensitivity vs. specificity as a Receiver Operator Characteristic (ROC) curve.