Search results “Roc curve interpretation”

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).
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RESOURCES:
- Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/
- Visualization: http://www.navan.name/roc/
- Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf
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Views: 246544
Data School

Produced for BST 230 at the University of Kentucky for educational purposes.
BMJ Article: http://dx.doi.org/10.1136/bmj.327.7417.716

Views: 19259
Jennifer Daddysman

My web page:
www.imperial.ac.uk/people/n.sadawi

Views: 13296
Noureddin Sadawi

Views: 10369
Crushed USMLE Questions

Sensitivity, specificity, tradeoffs and ROC curves. With a little bit of radar thrown in there for fun.

Views: 110824
Rahul Patwari

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.

Views: 54987
Dr. Todd Grande

My web page:
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Views: 42901
Noureddin Sadawi

Review: prediction success table. Sensitivity vs. Specificity. What is the ROC curve, and how is it used to evaluate model performance? Advantages of evaluating based on ROC. How to utilize the Area Under Curve (AUC).
http://www.salford-systems.com

Views: 42000
Salford Systems

Currell: Scientific Data Analysis. SPSS analysis for Fig 8.27 http://ukcatalogue.oup.com/product/9780198712541.do
© Oxford University Press

Views: 19708
Oxford Academic (Oxford University Press)

Includes an example with,
- logistic regression model
- confusion matrix
- misclassification rate
- rocr package
- accuracy versus cutoff curve
- identifying best cutoff values for best accuracy
- roc curve
- true positive rate (tpr) or sensitivity
- false positive rate (fpr) or '1-specificity'
- area under curve (auc)
Machine Learning videos: https://goo.gl/WHHqWP
roc curve is an important model evaluation tool related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 32118
Bharatendra Rai

In this episode, we show how to plot an ROC curve in Excel with the help of PrimaXL, an add-in software.
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Views: 635
FIAN Research

This video is part of an online course, Model Building and Validation. Check out the course here: https://www.udacity.com/course/ud919.

Views: 2897
Udacity

This videio will cover:
* what is a receiver operator curve.
* how to interpret a receiver operating characteristic curve.
* how to perform the calculations with Excel.
* how to graph the results with Excel.
Excel's pivot table tool is used to create a frequency distribution table. Another way to create the table is to use Excel's histogram tool. A video on how to create ROC curves using the histogram function has been posted at https://youtu.be/-rfzhtLOYq8.

Views: 17916
Stokes Baker

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
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Views: 726
ntvg

ROC Curve (Receiver Operating Characteristic Curve) and Random Oversampling Examples (ROSE Package) Analysis in R
1. Example Data Set LoanAnalysis.csv
https://drive.google.com/open?id=1a6VBAvhoprYFayIVpsaMNCK4CLSQK35y
2. Analysis Code
https://drive.google.com/open?id=1888o-tjgOkmAcpYfooqA8-GUOLrDSij5
3. Data Partition Analysis in R Lecture Video
https://www.youtube.com/watch?v=UFaZvynajtI
4. Logistic Regression Analysis in R Lecture Video
https://www.youtube.com/watch?v=eScK5w5JcHI
5. Decision Tree Analysis in R Example Tutorial Video
https://www.youtube.com/watch?v=bJC5S_ViRCo

Views: 8379
The Data Science Show

Determing the accuracy of a diagnostic-evaluative test in predicting a dichotomous outcome. For methods to determine a cut-off score for the diagnosis of the outcome, please see ROC Curve Part 2 (http://www.youtube.com/watch?v=WO8Re7YqnP0).
The following resource can be used to determine sample sizes for ROC analysis: Hanley JA, & McNeil BJ. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 143(1), 29-36.

Views: 122746
TheRMUoHP Biostatistics Resource Channel

Lecture 8-15 at https://vimeo.com/ondemand/logisticmodel/, available for paid subscription
In this video we cover the basics of Receiver Operating Curves (ROC) curves. The explanation shows how to calculate Sensitivity, 1-Specificity and plot a curve using excel.
We then cover the area under curve (AUC) of the ROC curve as a measure of the predictive power of the model and the apply that to both training and validation datasets and compare against each other to test stability of the model.

Views: 13564
Learn Analytics

In a ROC curve, we plot ‘True Positives‘ on Y-axis and ‘True Negatives‘ on X-axis. The average number of mistakes made while predicting the number of true positive values defines ROC(Receiver operating characteristic). How to make 0% mistake while identifying the positives, where AUC value nears to 1. AUC (Area under curve) is related to ROC. A detailed explanation is provided about ROC & AUC. Watch the video for more information on the topic.
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Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.
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Views: 362
Statinfer Analytics

An introduction to the calculation and use of ROC Curves and Area Under the Curve to accompany "Childhood forecasting of a segment of the adult population characterized by economic burden", Caspi, Houts, Belsky, Harrington, Hogan, Ramrakha, Poulton, & Moffitt (under review).

Views: 12079
moffittcaspi group

Determining a cut-off score for a diagnostic test using a ROC curve.

Views: 60291
TheRMUoHP Biostatistics Resource Channel

The video describes how to analyze data from a recognition memory experiment to create a Receiver Operating Characteristic (ROC) curve, which indicates how well the person is able to distinguish things they studied from things they didn't study.

Views: 19367
Sean Polyn

Correlation analysis, ROC curve, Likelihood ratios
Attached documents https://app.box.com/s/x49s8lodpu15wgzsr1dl0popblf0u3by

Views: 1076
Mohamed Saadeldin

In this video you will learn plotting ROC curve while doing Logistic Regression in SAS. You will also learn how to interpret a ROC Curve
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Views: 7571
Analytics University

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.

Views: 1202
NCSS Statistical Software

ROC curves produced from different classifiers are a good means to compare classifier performances. This session demonstrates the use of Knowledge-flow environment of Weka to generate multiple ROC curves for more than one classifiers.
Tutorial 28 shows how to generate a single ROC curve for a single classifier using Weka Explorer. The tutorial can be found at http://www.youtube.com/watch?v=j97h_-b0gvw

Views: 21620
Rushdi Shams

This is a slecture for Prof. Boutin's course on Statistical Pattern Recognition (ECE662) made by Purdue student Jianxin Sun. The complete slecture is posted at
https://www.projectrhea.org/rhea/index.php/ROC_curve_analysis_slecture_ECE662_Spring0214_Sun
To view other slectures on the same topic, go to the ECE662 course wiki at https://www.projectrhea.org/rhea/index.php/2014_Spring_ECE_662_Boutin
For more information about slectures, go to http://slectures.projectrhea.org

Views: 1366
Project Rhea

In this video you will learn the theory about ROC curve. ROC curve is used to assess predictive power of a Logistic Regression Model (any binary model for that matter)
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Views: 7919
Analytics University

This is a companion movie to the chapter on Receiver-Operator curves in "Interactive Mathematics for Laboratory Medicine" by Prof. T.S. Pillay. Available here: https://itunes.apple.com/us/book/interactive-mathematics-for/id1038925720?mt=11

Views: 48478
Kzn Elearning

Here you will learn how to fit a decision tree model in R and how to do predictions and get the probabilities for each classes and then how to plot a ROC curve in R.
This channel includes machine learning algorithms and implementation of machine learning algorithms in R like random forest algorithm in R,neural networks algorithms in R,decision tree in R and so on.Please do subscribe and like this channel for more videos on advances topics like deeplearning,graph theory,etc.

Views: 5026
Data Science by Arpan Gupta IIT,Roorkee

There are 2 test I can use to see if this patient has got cancer, which one is best? How do I know? How can I compare them?? These were just some of the thoughts going through the candidates mind as his stared at the paper in the academic viva in national selection!
If only they'd listened to Rob Radcliffe, who is on hand to explain how you do just that using receiver operating characteristic curves, a really easy way to compare the performance of tests and probably the most useful to medicine thing that had its origin in WW II radar technology.
Starting with a review of sensitivity and specificity (see http://schoolofsurgery.podomatic.com/entry/2014-05-02T00_31_49-07_00 for full revision) Rob shows how sensitivity and specificity vary with the cut off point for a test and demonstrates the best test you can design and the worst and shows you how to construct a ROC curve. Real life examples are discussed and how to compare test visually from their curves, and how this can be qualified (and so compared statistically to find the best performing test) using Area Under the Curve (AUC) is also explained.
This is the clearest explanation you will find anywhere for this commonly used comparison (check out the Wikipedia page on this if you don't believe me). Is is essential to know as ROC curve feature often in medical literature and often in exams and academic interviews.
Rob Radcliffe was a maths teacher in a former life and is now a trainee in Urology in the East Midlands, UK

Views: 7515
school of surgery

The video describes how to analyze data from a recognition memory experiment to create a Receiver Operating Characteristic (ROC) curve, which indicates how well the person is able to distinguish things they studied from things they didn't study. We don't get too far into the theory here, this really will just let you see how to do the simple calculations that let you create the ROC curve! (this is part I where we set up the problem, in part II we actually plot the ROC)

Views: 54833
Sean Polyn

Views: 20815
Stata Learner

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2jDsrGS].
Our goal in this video would be to understand logistic regression, evaluation metrics of binary classification problems, and interpretation of the ROC curve.
• Explain the concept behind logistic regression
• Understand the evaluation metrics and interpretation of the ROC curve
• Implement in R
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Views: 1554
Packt Video

In this video, you'll learn how to properly evaluate a classification model using a variety of common tools and metrics, as well as how to adjust the performance of a classifier to best match your business objectives. I'll start by demonstrating the weaknesses of classification accuracy as an evaluation metric. I'll then discuss the confusion matrix, the ROC curve and AUC, and metrics such as sensitivity, specificity, and precision. By the end of the video, you will have a solid foundation for intelligently evaluating your own classification model.
Download the notebook: https://github.com/justmarkham/scikit-learn-videos
== CONFUSION MATRIX RESOURCES ==
Simple guide to confusion matrix terminology: https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/
Intuitive sensitivity and specificity: https://www.youtube.com/watch?v=U4_3fditnWg
The tradeoff between sensitivity and specificity: https://www.youtube.com/watch?v=vtYDyGGeQyo
How to calculate "expected value" from a confusion matrix: https://github.com/podopie/DAT18NYC/blob/master/classes/13-expected_value_cost_benefit_analysis.ipynb
Classification threshold graphic: https://media.amazonwebservices.com/blog/2015/ml_adjust_model_1.png
== ROC/AUC RESOURCES ==
ROC Curves and Area Under the Curve: https://www.youtube.com/watch?v=OAl6eAyP-yo
ROC visualization: http://www.navan.name/roc/
ROC Curves: https://www.youtube.com/watch?v=21Igj5Pr6u4
An introduction to ROC analysis: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf
Comparing different feature sets: http://research.microsoft.com/pubs/205472/aisec10-leontjeva.pdf
Comparing different classifiers: http://www.cse.ust.hk/nevinZhangGroup/readings/yi/Bradley_PR97.pdf
== OTHER RESOURCES ==
scikit-learn documentation on model evaluation: http://scikit-learn.org/stable/modules/model_evaluation.html
Comparing model evaluation procedures and metrics: https://github.com/justmarkham/DAT8/blob/master/other/model_evaluation_comparison.md
Counterfactual evaluation of machine learning models: https://www.youtube.com/watch?v=QWCSxAKR-h0
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Views: 63669
Data School

"The ROC Curve and the Area under the Curve (AUC),” Shimin Zheng, Ph.D.
ETSU Psychiatry Grand Rounds 2.17.17

Views: 239
ETSU CME Grand Rounds

This tutorial demonstrates how to produce a single ROC curve for a single classifier. It also demonstrates how to get the Area under ROC curve or (AUC). ROC curves are cost-sensitive measures to evaluate classifier performance. However, it is not a good mesure of model goodness if the dataset is imbalanced (highly skewed class distributions are present).
LinkedIn: http://www.linkedin.com/pub/rushdi-shams/3b/83b/9b3

Views: 70107
Rushdi Shams

Recorded from http://demonstrations.wolfram.com/HowReceiverOperatingCharacteristicCurvesWork/

Views: 581
Matthew Neal

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Views: 2496
Biochem

In this video you will learn about the different performance matrix used for model evaludation such as Receiver Operating Charateristics, Confusion matrix, Accuracy. This is used very well in evauating classfication models like deicision tree, Logistic regression, SVM
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Big Edu

Machine Learning #51 ROC Curve
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Views: 867
Xoviabcs

This Video talks about how to decide a value of threshold to convert Probabilities into classes in a Classification Problem.
This video is part of a Self Paced course on Mydatacafe. Please visit www.mydatacafe.com if you want to enroll into any of our courses. Subscribe for more sch free Videos on Data Science.

Views: 243
MyDataCafe

In this video you will learn how to use ROC curves to select the best fit model out of a range of model.
Visit : http://analyticuniversity.com/

Views: 1193
Analytics University

Enter sensitivity and specificity and use the calculator to make a scatterplot with connect lines.

Views: 14945
Jenny Shook

An ROC curve is a plot that compares the trade off of true positives and false positives of a binary classifier under different thresholds. The area under the curve (AUC) is useful in determining how discriminating a model is. Together, ROC and AUC are very useful diagnostics for understanding the power of one's model and how to tune it.

Views: 375
Data Skeptic

© 2018 Play online barbie fashion games

Trigger Types. A DML trigger is fired by a DML statement, a DDL trigger is fired by a DDL statement, a DELETE trigger is fired by a DELETE statement, and so on. An INSTEAD OF trigger is a DML trigger that is defined on a view (not a table). The database fires the INSTEAD OF trigger instead of executing the triggering DML statement. For more information, see Modifying Complex Views (INSTEAD OF Triggers). A system trigger is defined on a schema or the database. A trigger defined on a schema fires for each event associated with the owner of the schema (the current user). A trigger defined on a database fires for each event associated with all users. A simple trigger can fire at exactly one of the following timing points : Before the triggering statement executes. After the triggering statement executes. Before each row that the triggering statement affects. After each row that the triggering statement affects. A compound trigger can fire at more than one timing point. Compound triggers make it easier to program an approach where you want the actions you implement for the various timing points to share common data. For more information, see Compound Triggers. Trigger States.