(Basic Statistics for Citizen Data Scientist)
AUC Confidence Interval
For large samples, AUC (area under the curve for a ROC curve) is approximately normally distributed, and so a 1-α confidence interval for AUC may be calculated as described in Confidence Interval for Sampling Distributions.
The confidence interval is equal to AUC ± se · zcrit where zcrit is the two-tailed critical value of the standard normal distribution, as calculated in Excel by =NORM.S.INV(1-α/2) and
where n1 and n2 are the sizes of the two samples and
Example 1: Find the 95% confidence for the AUC from Example 1 of Classification Table.
From Figure 1 of ROC Curve, we see that n1 = 527, n2 = 279 and AUC = .88915. The 95% confidence interval of AUC is (.86736, .91094), as shown in Figure 1.
Figure 1 – AUC 95% confidence Interval
Real Statistics Functions: The Real Statistics Resource Pack contains the following functions:
AUC_LOWER(auc, n1, n2, α) = the lower limit of the 1-α confidence interval for the area under the curve = auc for samples of size n1 and n2
AUC_UPPER(auc, n1, n2, α) = the upper limit of the 1-α confidence interval for the area under the curve = auc for samples of size n1 and n2
If the α argument is omitted it defaults to .05.
For Example 1, we see that =AUC_LOWER(B5, B3, B4) calculates the value shown in cell B12 and =AUC_UPPER(B5, B3, B4) calculates the value shown in cell B13.
Statistics for Beginners in Excel – AUC Confidence Interval
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:
All Notebooks in One Bundle: Data Science Recipes and Examples in Python & R.
End-to-End Python Machine Learning Recipes & Examples.
End-to-End R Machine Learning Recipes & Examples.
Applied Statistics with R for Beginners and Business Professionals
Data Science and Machine Learning Projects in Python: Tabular Data Analytics
Data Science and Machine Learning Projects in R: Tabular Data Analytics
Python Machine Learning & Data Science Recipes: Learn by Coding
R Machine Learning & Data Science Recipes: Learn by Coding
Comparing Different Machine Learning Algorithms in Python for Classification (FREE)
There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $29.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.