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Mean, or Expected Value - is a theoretical property of a certain probability. Average is the observed/measured outcome of a certain sample. If a measured average diverge too much from the expected mean, it's a sign that the underlying probability assumption, or one of its properties, is wrong. This is the main distinction between the terms that ...
$\begingroup$ @dkr, You might want to ask this as a new question to get more (and more in-depth) responses. That said, the difference boils down to two things: 1) the thing to be minimized (squared distance/L2 norm for the centroid, absolute distance/L1 norm for mediod) and 2) Whether the output can be any point (centroid) or must be in the data set (mediod).
1. @trevorDashDash One reason would seem to be that it doesn't always make sense to assume that the intercept is 0 0. (Think of one-time fees as an example.) – Chill2Macht. Mar 23, 2017 at 20:21. Add a comment. ri yi/xi r i y i / x i. Improve this answer. edited.
As an example: If you drive from New York to Boston at 40 MPH, and return at 60 MPH, then your overall average is not the arithmetic mean of 50 MPH, but the harmonic mean. AM = (40 + 60)/2 = 50 (40 + 60) / 2 = 50 HM = 2/(1/40 + 1/60) = 48 2 / (1 / 40 + 1 / 60) = 48. to check that this is right for this simple example, imagine it is 120 miles ...
$\begingroup$ My concern is that factual characteristics of the mean and median (e.g. the former is sensitive to outliers, viz "People of such age have 1.5 to 4 times the influence on the mean than they do on the median compared to very young people.") become translated into values about their worth, viz "the median gives us a slightly better picture of what the age distribution itself looks ...
In the first case (mean of means), we have an "average" hit rate of 0.5001/2 while in the second case (mean of total) we have 3/10003, and these two numbers are not the same. Whether one is more appropriate or correct depends on your use case.
I recently read a very nice article by Nicholas Vandeput on LinkedIn wherein he linked the forecast type to use of different best fit selection criteria. Optimization on RMSE yields an average-based number... whereas on MAE yields a median-based forecast. Forecast KPI: RMSE, MAE, MAPE & Bias. Advantages of using median forecast: robust to outliers.
For Power Law distributions it usually makes sense to look at the logarithms. In the case of log-normally distributed data .... the geometric mean is a better measure of central tendancy than the arithmetic mean. I mean I would guess they look at the paper and have seen a log-normal distribution. Spots ... makes me think its referring to probes ...
Improve this question. edited Oct 25, 2017 at 2:34. Ferdi. 5,257 10 47 64. asked Dec 7, 2016 at 15:31. Zara. 121 1 1 3. Presumably in one case you takes logs and then the mean (which is the geometric mean when anti-logged) and in the other you reverse the ordering of operations. They are not the same, as indeed you hint.
An example confusion matrix to calculate Class Accuracy and Overall Accuracy: According to the references given in answer mean accuracy can be calculated as: Mean Accuracy of Class N: 1971/ (1971 + 19 + 1 + 8 + 0 + 1) = 98.55%. Overall accuracy = (1971 + 1940 + 1891 + 1786 + 1958 + 1926) / (2000 + 2000 + 2000 + 2000 + 2000 + 2000) = 95.60.