Instrumentation & Measurement Magazine 24-2 - 90

Table 2 - Comparing I&M and ML terminologies (continued)
ML regression
Term

Bias

meaning in I&M

difference between the
expectation of the test results
and reference value [15, 3.8];
the total systematic error
[15, 3.8 Note 5]; estimate of a
systematic error [11, 2.18]

meaning

relation with I&M

root-mean square deviation
of the expected value of the
estimator (over all fixed-size
training datasets) from the
true value [12, p.13]; the part
of error that is increased by
underfitting the model; can be
expressed as [12, eq. 7]:

False friends!
ML bias is model bias,
indicating the root-mean
square deviation from the true
value over the whole dataset,
whereas I&M bias is for a
single measurand at run time.

 f (x )  E
S

i

i1

Variance

Noise

same as statistical variance;
sometimes used to describe
the variance of repeated
measurements, although
standard deviation, and not
variance, is used to quantify
uncertainty

D



2



meaning

relation with
I&M

✘

 fˆ ( x ) ​
 Dj i 

fluctuations in the estimator's
output [12, p.13]; the part
of error that is increased by
overfitting the model; can be
expressed as [12, eq. 8]:
2 ​

​S
ED  fˆD j  xi   ED  fˆDj  xi  


 
i1


not an official I&M term in
VIM [11]; may commonly
refer to the noise signal known
from Electrical Engineering;
contributes to random error

ML classification



a.k.a. irreducible error
For a single input (x) in the
dataset: random error or
variance measured in y for that
x [16, p.89]

False friends!
ML variance is model variance,
indicating the said fluctuations
over the whole dataset,
whereas I&M variance is for a
single measurand at run time.

✘

N/A

ML noise for a single data
point in the dataset is similar
to I&M random error, but not
I&M noise because the latter is
only one possible contributor
to I&M random error. Other
factors, such as fluctuations
in environmental conditions,
also contribute to I&M random
error.

✔

For the whole dataset: ​
S

Noise   2​[12, p.13]
i 1

False friends!
ML noise for the whole dataset
is model noise, whereas in
I&M, noise contributes to the
random error of measuring a
single measurand.

✘

in the context of a
measurement instrument:
closeness of a test result to the
reference value [15, 3.6]
Accuracy

in the context of measurement
results: closeness of a
measured value to the true
value [11, 2.13]; measurement
is more accurate when it offers
a smaller error [11, 2.13 Note 1]

widely-understood as the
closeness of the estimations
to their true values; model is
more accurate when it offers a
smaller error

As single input accuracy: when
considered for a single input/
output pair in ML, accuracy
represents how close the
estimation is to the reference/
true value, which is very
similar to I&M accuracy

✔
As model accuracy: false
friends; when considered for
the whole ML dataset, accuracy
is no longer an indication
of what happens to a single
specific input

TP  TN

​ PN ​

False friends!

TP

​ TP  FP ​

False friends!

✘

✘

Precision

rarely used, with no widelyaccepted definition; some
research works have tried to
address it [17]

closeness of repeated
measurement results to each
other [15, 3.12]

I&M precision is affected
by random error, to which a
variable-output ML's variance
of the estimation for a single
input contributes

✘

✔

Trueness

closeness of the average of
repeated measurement results
to the reference value [15, 3.7]

N/A

I&M trueness is affected by
systematic error, to which ML's
error (single-output ML error,
or variable-output ML outputs'
average error) of a single input
contributes

N/A

✔

90	

IEEE Instrumentation & Measurement Magazine	

April 2021



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