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h
ig
h
.
Ho
w
e
v
er
,
ap
p
ly
i
n
g
th
i
s
b
r
o
a
d
ly
ac
r
o
s
s
all
ch
an
n
el
s
w
it
h
o
u
t
k
n
o
w
led
g
e
o
f
t
h
e
cu
s
to
m
er
’
s
in
ter
ac
tio
n
b
e
h
av
io
r
ca
n
b
e
in
e
f
f
icie
n
t
a
n
d
co
s
tl
y
.
C
o
n
s
eq
u
en
tl
y
,
a
p
r
ed
ictiv
e
m
o
d
el
is
r
eq
u
ir
ed
to
f
o
r
ec
a
s
t
th
e
in
ter
ac
tio
n
s
th
at
cu
s
to
m
er
s
w
ill
r
esp
o
n
d
to
in
th
eir
p
r
ef
er
r
ed
ch
an
n
el
s
.
T
h
is
en
ab
les
XYZ
to
ef
f
ec
ti
v
el
y
tar
g
et
cu
s
to
m
er
s
w
ith
tailo
r
ed
ch
an
n
el
r
ec
o
m
m
en
d
atio
n
s
an
d
p
r
ed
ict
th
e
n
u
m
b
er
o
f
i
n
ter
ac
tio
n
s
.
T
h
e
is
s
u
e
s
d
escr
ib
ed
ab
o
v
e
in
d
icate
a
n
ee
d
f
o
r
a
d
ata
m
i
n
in
g
s
o
lu
tio
n
t
h
at
w
ill
en
ab
le
th
e
i
m
p
le
m
en
ta
tio
n
o
f
a
m
o
d
el
to
f
u
l
f
ill
t
h
e
cla
s
s
i
f
icatio
n
n
ee
d
s
o
f
C
I
S
an
d
p
r
o
v
id
e
r
ec
o
m
m
e
n
d
atio
n
s
r
eg
ar
d
in
g
ch
an
n
el
s
to
P
T
.
XYZ
.
C
o
n
v
en
tio
n
al
m
ac
h
i
n
e
lear
n
i
n
g
m
o
d
els
ar
e
in
ad
eq
u
ate
b
ec
a
u
s
e
th
e
y
n
ec
es
s
itat
e
h
y
p
er
p
ar
a
m
eter
o
p
ti
m
izatio
n
.
T
h
is
is
a
p
r
o
ce
s
s
o
f
id
en
ti
f
y
in
g
t
h
e
m
o
s
t
o
p
ti
m
al
h
y
p
er
p
ar
a
m
eter
s
f
o
r
a
m
ac
h
i
n
e
lear
n
in
g
m
o
d
el.
T
h
er
ef
o
r
e,
th
e
u
tili
za
tio
n
o
f
h
y
p
er
p
ar
a
m
ete
r
o
p
tim
izat
io
n
m
a
y
r
es
u
lt
i
n
a
n
en
h
a
n
ce
d
m
o
d
el
w
it
h
e
n
h
a
n
ce
d
p
er
f
o
r
m
a
n
ce
.
T
h
e
m
a
ch
in
e
l
ea
r
n
in
g
al
g
o
r
ith
m
u
t
il
i
z
e
d
in
th
is
r
e
s
e
a
r
ch
is
r
an
d
o
m
f
o
r
es
t
(
R
F
)
,
a
p
p
l
i
e
d
t
o
c
la
s
s
if
y
C
I
S
i
n
th
e
c
o
n
t
ex
t
o
f
c
r
e
d
i
t
s
c
o
r
in
g
.
T
r
i
v
e
d
i
[
3
]
a
c
h
i
ev
e
d
h
ig
h
a
c
cu
r
a
cy
w
ith
R
F
o
n
G
e
r
m
an
c
r
e
d
i
t d
a
t
a
u
s
in
g
f
e
a
tu
r
e
s
e
l
e
ct
i
o
n
te
ch
n
i
q
u
e
s
,
s
u
ch
a
s
C
h
i
-
s
q
u
a
r
e
(
9
3
.
1
2
%
)
,
G
a
in
-
R
a
t
io
(
9
1
.
2
0
%
)
,
an
d
I
n
f
o
-
G
a
in
(
9
0
.
9
0
%
)
.
Fu
r
th
e
r
m
o
r
e
,
Xu
e
t
a
l
.
[
4
]
d
em
o
n
s
t
r
at
e
d
th
a
t
R
F
a
ch
i
ev
e
d
a
n
a
cc
u
r
a
cy
o
f
9
8
.
4
%
f
o
r
b
o
r
r
o
w
e
r
d
e
f
au
l
t
r
i
s
k
o
n
a
P2
P
p
l
a
t
f
o
r
m
in
C
h
in
a
,
an
d
L
i
an
d
C
h
en
[
5
]
r
e
p
o
r
t
e
d
8
1
.
0
5
%
o
n
a
l
en
d
in
g
c
lu
b
c
o
n
s
u
m
e
r
c
r
e
d
i
t
l
o
an
s
d
a
t
a
s
et
in
Q
4
2
0
1
8
.
A
d
d
it
i
o
n
a
l
ly
,
th
e
r
es
e
a
r
ch
em
p
l
o
y
s
t
h
e
K
-
n
ea
r
e
s
t
n
e
ig
h
b
o
r
s
(
K
N
N
)
r
eg
r
e
s
s
o
r
f
o
r
c
h
a
n
n
el
r
e
c
o
m
m
en
d
a
ti
o
n
s
a
n
d
c
u
s
t
o
m
e
r
in
t
e
r
ac
t
i
o
n
p
r
e
d
i
c
ti
o
n
.
P
atr
o
et
a
l.
[
6
]
in
tr
o
d
u
ce
d
a
h
y
b
r
id
ac
tio
n
-
r
elate
d
K
-
n
ea
r
es
t
n
eig
h
b
o
r
s
(
H
A
R
-
KNN)
m
et
h
o
d
f
o
r
p
r
o
d
u
ct
r
ec
o
m
m
e
n
d
atio
n
s
b
ased
o
n
Am
az
o
n
d
ata.
T
h
is
in
co
r
p
o
r
ated
th
e
KNN
alg
o
r
it
h
m
a
n
d
ac
h
iev
ed
a
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
o
f
0
.
7
1
6
5
w
it
h
1
0
n
eig
h
b
o
r
s
.
J
e
n
a
et
a
l
.
[
7
]
d
em
o
n
s
t
r
a
t
e
d
th
at
K
NN
p
r
o
d
u
c
e
d
t
h
e
b
e
s
t
p
e
r
f
o
r
m
an
c
e
w
it
h
MA
E
s
o
f
0
.
0
1
3
0
i
n
e
-
L
ea
r
n
in
g
c
o
u
r
s
e
r
e
c
o
m
m
en
d
a
t
i
o
n
.
T
h
is
r
esear
ch
also
in
co
r
p
o
r
ates
h
y
p
er
p
ar
a
m
eter
o
p
ti
m
izatio
n
,
s
p
ec
if
ica
ll
y
g
r
id
s
ea
r
ch
an
d
B
a
y
esia
n
o
p
ti
m
iza
ti
o
n
,
to
r
ef
in
e
m
o
d
el
ac
cu
r
ac
y
.
P
r
ev
io
u
s
l
y
,
L
i
an
d
C
h
e
n
[
5
]
ap
p
lied
g
r
id
s
ea
r
ch
to
an
R
F
m
o
d
el
f
o
r
cr
ed
it
lo
an
s
,
ac
h
iev
in
g
an
ac
cu
r
ac
y
o
f
8
1
.
0
5
%.
Sh
a
m
s
et
a
l.
[
8
]
ap
p
lied
it
t
o
a
KNN
r
eg
r
ess
o
r
m
o
d
el
f
o
r
w
ater
q
u
ali
t
y
in
I
n
d
ia,
ac
h
iev
in
g
a
MA
E
o
f
0
.
0
0
0
9
.
Fu
r
th
er
m
o
r
e,
W
an
g
et
a
l.
[
9
]
o
p
tim
ized
an
R
F
m
o
d
el
w
it
h
B
a
y
e
s
ian
o
p
t
i
m
izatio
n
,
ac
h
ie
v
i
n
g
an
ac
cu
r
ac
y
o
f
9
9
.
3
0
%
in
cy
b
e
r
-
p
h
y
s
ical
s
y
s
te
m
s
,
w
h
i
le
L
ee
et
a
l.
[
1
0
]
em
p
lo
y
ed
th
e
s
a
m
e
m
et
h
o
d
t
o
o
p
tim
ize
a
KNN
m
o
d
el,
p
r
ed
ictin
g
r
ain
f
all
er
o
s
i
v
it
y
in
I
tal
y
an
d
S
w
i
t
ze
r
lan
d
w
i
th
a
M
A
E
o
f
2
0
.
5
7
6
MJ
m
m
h
a
-
1
h
-
1.
T
h
ese
r
esear
ch
co
n
tr
ib
u
tio
n
s
i
n
clu
d
e:
(
i)
e
x
p
lo
r
in
g
th
e
ap
p
li
ca
tio
n
o
f
C
I
S
clas
s
i
f
icatio
n
an
d
ch
an
n
el
r
ec
o
m
m
e
n
d
atio
n
p
r
ed
ictio
n
,
(
ii)
u
s
in
g
c
u
s
to
m
er
d
ata
f
r
o
m
a
P
2
P
len
d
in
g
co
m
p
a
n
y
an
d
co
m
m
u
n
icatio
n
ch
an
n
el
s
p
r
o
v
id
ed
b
y
XY
Z
f
o
r
th
e
p
er
io
d
J
u
l
y
2
0
2
3
u
n
ti
l
Dec
e
m
b
er
2
0
2
3
,
(
iii)
im
p
le
m
en
t
in
g
clas
s
i
f
icatio
n
an
d
p
r
ed
ictio
n
m
o
d
els
u
s
i
n
g
t
h
e
R
F
alg
o
r
ith
m
f
o
r
C
I
S
an
d
KNN
r
eg
r
ess
o
r
f
o
r
ch
an
n
e
l
r
ec
o
m
m
en
d
atio
n
,
(
iv
)
o
p
ti
m
izi
n
g
t
h
e
R
F
an
d
K
NN
r
eg
r
ess
o
r
m
o
d
els
t
h
r
o
u
g
h
h
y
p
er
p
ar
a
m
eter
o
p
ti
m
iza
tio
n
u
s
i
n
g
g
r
id
s
ea
r
ch
a
n
d
B
ay
e
s
ian
o
p
ti
m
iza
tio
n
,
a
n
d
(
v
)
co
m
p
ar
in
g
t
h
e
R
F
a
n
d
KNN
r
eg
r
ess
o
r
m
o
d
el
s
a
f
ter
g
r
id
s
ea
r
ch
an
d
B
a
y
esia
n
o
p
tim
izatio
n
i
m
p
le
m
en
ta
tio
n
s
to
d
eter
m
in
e
t
h
e
b
est
o
p
ti
m
iz
atio
n
t
y
p
e
f
o
r
C
I
S
an
d
ch
a
n
n
el
r
ec
o
m
m
e
n
d
atio
n
b
ased
o
n
ac
cu
r
ac
y
an
d
M
A
E
m
etr
ics.
2.
M
E
T
H
O
D
T
h
is
r
esear
ch
ad
h
er
es
to
th
e
s
tag
e
s
d
elin
ea
ted
in
th
e
cr
o
s
s
in
d
u
s
tr
y
s
tan
d
ar
d
p
r
o
ce
s
s
-
d
ata
m
in
i
n
g
(
C
R
I
SP
-
DM
)
f
r
a
m
e
w
o
r
k
.
T
h
e
s
ta
g
es
u
s
ed
i
n
clu
d
e
b
u
s
in
es
s
u
n
d
er
s
ta
n
d
in
g
,
d
ata
u
n
d
er
s
tan
d
i
n
g
,
d
ata
p
r
ep
ar
atio
n
,
m
o
d
elin
g
,
an
d
ev
alu
atio
n
.
Ho
w
e
v
er
,
th
e
d
ep
lo
y
m
en
t stag
e
i
s
n
o
t e
m
p
lo
y
ed
,
as
th
is
s
t
u
d
y
is
s
o
lel
y
co
n
ce
r
n
ed
w
i
th
t
h
e
co
m
p
ar
is
o
n
o
f
o
p
ti
m
izatio
n
m
et
h
o
d
s
f
o
r
th
e
d
ev
elo
p
ed
m
o
d
els.
Fu
r
t
h
er
m
o
r
e,
t
h
is
r
esear
c
h
e
m
p
lo
y
s
t
h
e
ex
tr
ac
t,
tr
an
s
f
o
r
m
,
lo
ad
(
E
T
L
)
p
r
o
ce
s
s
f
o
r
th
e
d
ata
p
r
ep
ar
atio
n
p
h
ase
w
it
h
in
t
h
e
C
R
I
SP
-
DM
f
r
a
m
e
w
o
r
k
.
T
h
e
w
o
r
k
f
lo
w
o
f
t
h
e
C
I
S a
n
d
ch
a
n
n
el
r
ec
o
m
m
e
n
d
atio
n
r
esear
ch
is
d
ep
icted
in
Fig
u
r
e
1
.
2
.
1
.
B
us
ines
s
un
der
s
t
a
nding
I
n
th
e
cu
r
r
en
t
p
h
ase
o
f
b
u
s
i
n
e
s
s
u
n
d
er
s
tan
d
i
n
g
,
th
e
o
b
j
ec
tiv
es
o
f
XYZ
in
cl
u
d
e
th
e
id
en
ti
f
icatio
n
o
f
cu
s
to
m
er
i
n
ten
s
it
y
ca
te
g
o
r
ies
in
t
h
e
C
I
S
s
y
s
te
m
a
n
d
th
e
p
r
o
v
is
io
n
o
f
ap
p
r
o
p
r
iate
tr
ea
t
m
en
t
b
ased
o
n
th
e
r
ec
o
m
m
e
n
d
ed
co
m
m
u
n
icatio
n
c
h
an
n
els.
A
d
d
itio
n
al
l
y
,
t
h
e
co
llectio
n
o
f
u
n
p
aid
lo
an
p
a
y
m
e
n
t
s
f
r
o
m
P
2
P
l
en
d
in
g
cu
s
to
m
er
s
is
a
k
e
y
o
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2
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3
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1
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[
1
1
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.
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[
4
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h
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d
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tr
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tio
n
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1
2
]
.
I
n
th
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cla
s
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f
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ap
p
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2
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4
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1
.
R
a
nd
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m
f
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t
RF
is
an
e
n
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m
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ased
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B
r
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an
in
2
0
0
1
[
1
3
]
.
RF
em
p
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s
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p
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ed
lear
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in
g
m
eth
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in
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m
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tio
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a
lab
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ataset
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tr
ain
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et)
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tili
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to
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k
e
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ed
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s
an
d
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s
tr
u
ct
a
m
o
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el
[
1
4
]
.
I
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th
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co
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tr
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5
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tco
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[
1
6
]
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th
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ar
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f
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[
1
5
]
.
G
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lcu
late
d
as in
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2
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K
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d
r
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1
7
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KNN
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1
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[
1
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T
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c
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Gr
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Gr
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B
a
y
esia
n
o
p
ti
m
izati
o
n
w
o
r
k
s
w
e
ll
w
h
e
n
th
e
clas
s
i
f
icatio
n
d
ataset
i
s
n
o
n
-
li
n
ea
r
,
co
m
p
lex
,
an
d
n
o
is
y
b
ec
au
s
e
th
e
co
m
p
u
tatio
n
to
id
en
tify
co
s
tl
y
h
y
p
er
p
ar
a
m
eter
s
ca
n
s
i
g
n
i
f
ic
an
tl
y
i
m
p
ac
t
m
o
d
el
p
er
f
o
r
m
an
ce
[
2
3
]
.
T
h
e
B
ay
esia
n
o
p
ti
m
izat
io
n
p
r
o
ce
s
s
in
v
o
l
v
es
t
w
o
m
ai
n
s
tep
s
,
s
u
ch
as
esti
m
ati
n
g
t
h
e
b
lack
-
b
o
x
f
u
n
ct
io
n
f
r
o
m
t
h
e
d
ata
b
y
a
p
r
o
b
ab
ilis
tic
s
u
r
r
o
g
ate
m
o
d
el
u
s
i
n
g
Ga
u
s
s
ian
p
r
o
ce
s
s
es,
k
n
o
w
n
as
th
e
r
esp
o
n
s
e
s
u
r
f
ac
e,
an
d
m
a
x
i
m
izi
n
g
an
ac
q
u
is
i
tio
n
f
u
n
ctio
n
to
b
alan
ce
th
e
ex
p
lo
r
atio
n
-
e
x
p
lo
itatio
n
tr
ad
eo
f
f
b
ased
o
n
th
e
u
n
ce
r
tain
t
y
a
n
d
o
p
tim
al
it
y
o
f
t
h
e
r
esp
o
n
s
e
s
u
r
f
ac
e
[
2
4
]
.
B
ay
esia
n
o
p
tim
izatio
n
in
co
r
p
o
r
ates
p
r
io
r
in
f
o
r
m
a
tio
n
ab
o
u
t
th
e
f
u
n
ctio
n
f
an
d
u
p
d
ates
p
o
s
ter
io
r
in
f
o
r
m
atio
n
th
at
h
elp
s
r
ed
u
c
e
lo
s
s
an
d
m
ax
i
m
ize
m
o
d
el
ac
cu
r
ac
y
[
2
3
]
.
Gau
s
s
ia
n
p
r
o
ce
s
s
f
r
o
m
B
a
y
es
ian
o
p
tim
izatio
n
i
s
co
m
p
u
ted
as i
n
(
5
)
[
2
5
]
:
(
)
~
(
(
)
,
(
,
′
)
)
(
5
)
Gau
s
s
ia
n
p
r
o
ce
s
s
eq
u
atio
n
ass
u
m
es
a
p
r
io
r
d
is
tr
ib
u
tio
n
as
a
m
u
lti
v
ar
iate
n
o
r
m
a
l
d
is
tr
ib
u
tio
n
w
i
th
a
s
p
ec
if
ic
m
ea
n
v
ec
to
r
an
d
co
v
a
r
ian
ce
m
atr
ix
.
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h
is
eq
u
atio
n
i
n
clu
d
es
x
a
s
a
p
o
in
t
in
th
e
in
p
u
t
s
p
ac
e,
(
)
as
t
h
e
m
ea
n
f
u
n
ctio
n
f
o
r
th
e
m
ea
n
v
ec
to
r
,
an
d
(
,
′
)
as
th
e
co
v
ar
ian
ce
f
u
n
ct
io
n
o
r
k
er
n
el
f
o
r
ea
ch
p
air
o
f
p
o
in
ts
x
an
d
′
f
o
r
th
e
co
v
ar
ian
ce
m
atr
ix
.
T
h
is
k
er
n
e
l
is
ch
o
s
e
n
s
o
th
at
t
h
e
p
o
in
ts
x
an
d
′
h
a
v
e
a
h
i
g
h
p
o
s
iti
v
e
co
r
r
elatio
n
,
in
d
icatin
g
t
h
at
t
h
e
y
s
h
o
u
ld
h
av
e
m
o
r
e
s
i
m
ilar
f
u
n
ctio
n
v
al
u
es
t
h
a
n
p
o
in
ts
f
u
r
t
h
er
ap
ar
t.
Nex
t,
th
e
p
o
s
ter
io
r
p
r
o
b
ab
ilit
y
f
u
n
ctio
n
i
s
co
m
p
u
ted
as
a
co
n
d
itio
n
al
d
is
tr
ib
u
tio
n
to
ev
alu
ate
th
e
v
alu
e
o
f
(
)
an
d
p
r
ed
ict
it
at
s
o
m
e
n
e
w
p
o
i
n
ts
∗
.
T
h
e
ca
lcu
latio
n
o
f
t
h
i
s
p
o
s
ter
io
r
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
in
cl
u
d
es
t
h
e
p
o
s
ter
io
r
m
ea
n
an
d
th
e
p
o
s
ter
io
r
v
ar
i
an
ce
,
w
h
ich
ar
e
co
m
p
u
ted
as i
n
(
6
)
-
(
8
)
[
2
5
]
:
(
∗
)
|
(
)
~
(
(
∗
)
,
2
(
∗
)
)
(
6
)
(
∗
)
=
(
∗
,
)
[
(
,
)
+
2
]
−
1
(
7
)
2
(
∗
)
=
(
∗
,
∗
)
−
(
∗
,
)
[
(
,
)
+
2
]
−
1
(
,
∗
)
(
8
)
As
s
h
o
w
n
in
(
6
)
is
th
e
p
o
s
ter
io
r
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
t
io
n
eq
u
atio
n
,
w
h
er
e
(
∗
)
is
th
e
p
o
s
ter
io
r
m
ea
n
a
n
d
2
(
∗
)
is
th
e
p
o
s
ter
io
r
v
ar
i
an
ce
.
T
h
e
p
o
s
ter
io
r
m
ea
n
i
n
(
7
)
is
a
w
ei
g
h
ted
av
er
ag
e
b
et
w
ee
n
th
e
p
r
io
r
m
ea
n
(
)
an
d
th
e
p
r
ed
ictio
n
b
ase
d
o
n
th
e
o
b
s
er
v
ed
d
ata
(
)
,
w
h
er
e
th
e
w
ei
g
h
t
s
d
ep
en
d
o
n
th
e
k
er
n
el.
T
h
is
eq
u
atio
n
i
n
cl
u
d
es
(
∗
,
)
as
th
e
co
v
a
r
ian
ce
b
et
w
ee
n
t
h
e
p
r
ed
ictio
n
p
o
in
t a
n
d
t
h
e
o
b
s
er
v
ed
p
o
in
ts
,
(
,
)
as
th
e
co
v
ar
ia
n
ce
b
et
w
ee
n
t
h
e
o
b
s
er
v
ed
p
o
in
ts
,
a
n
d
2
as
th
e
n
o
is
e
v
ar
ian
ce
ad
j
u
s
ted
w
it
h
t
h
e
co
v
ar
ian
ce
(
,
)
an
d
Y
as
th
e
o
b
s
er
v
ed
tar
g
et
v
alu
es.
T
h
e
p
o
s
ter
io
r
v
ar
ian
ce
i
n
(
8
)
is
co
m
p
u
ted
b
y
tak
in
g
t
h
e
p
r
io
r
c
o
v
ar
ian
ce
(
,
′
)
an
d
ad
j
u
s
tin
g
it
b
y
s
u
b
tr
ac
tin
g
th
e
v
ar
ian
ce
f
r
o
m
t
h
e
o
b
s
er
v
ed
d
ata
(
)
.
T
h
e
ap
p
licat
i
o
n
o
f
th
e
a
c
q
u
i
s
i
t
io
n
f
u
n
c
ti
o
n
is
th
en
p
e
r
f
o
r
m
e
d
u
s
in
g
t
h
e
e
x
p
e
ct
e
d
im
p
r
o
v
em
en
t
t
o
c
a
p
t
u
r
e
t
h
e
ex
p
e
c
te
d
v
a
lu
e
o
f
th
e
im
p
r
o
v
em
en
t
an
d
s
el
e
c
t
x
t
o
m
ax
im
i
ze
i
t
[
2
5
]
.
T
h
e
r
e
s
u
l
ts
o
f
th
i
s
a
p
p
l
i
c
a
ti
o
n
a
r
e
u
s
e
d
t
o
u
p
d
a
t
e
th
e
G
au
s
s
i
an
p
r
o
c
es
s
s
ta
t
i
s
t
i
c
al
m
o
d
el
[
2
3
]
.
E
x
p
e
ct
e
d
im
p
r
o
v
em
en
t
is
p
e
r
f
o
r
m
e
d
as in
(
9
)
[
2
5
]
.
(
)
=
[
(
(
)
−
(
+
)
,
0
)
]
(
9
)
Evaluation Warning : The document was created with Spire.PDF for Python.
T
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w
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[
2
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itiv
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i
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v
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I
n
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s
et
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o
[
9
]
.
2
.
5
.
E
v
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lua
t
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m
et
rics
E
v
alu
a
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etr
ics
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c
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t t
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th
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lass
if
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tio
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p
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d
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p
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f
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a
n
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in
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h
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tu
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y.
2
.
5
.
1
.
Acc
ura
cy
A
cc
u
r
ac
y
i
s
th
e
p
r
o
p
o
r
tio
n
o
f
all
d
ata
th
at
is
ac
c
u
r
atel
y
cl
ass
i
f
ied
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h
e
h
ig
h
er
th
e
ac
c
u
r
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b
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ed
f
r
o
m
a
m
o
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el,
th
e
b
et
ter
th
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p
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f
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r
m
an
ce
o
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th
e
m
o
d
el.
A
cc
u
r
ac
y
ca
n
b
e
o
b
tain
ed
in
(
1
0
)
:
=
+
+
+
+
(
1
0
)
w
h
er
e
is
tr
u
e
p
o
s
iti
v
e
,
is
tr
u
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n
eg
ati
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f
alse p
o
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iti
v
e
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d
is
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alse
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eg
at
iv
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[
3
]
.
2
.
5
.
2
.
P
re
cisi
o
n
P
r
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is
io
n
in
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icate
s
h
o
w
m
a
n
y
s
a
m
p
les
ar
e
p
r
ed
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to
b
e
p
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v
e
o
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tr
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p
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ca
n
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e
o
b
tain
ed
in
(
1
1
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[
4
]
.
=
+
(
1
1
)
2
.
5
.
3
.
Rec
a
ll
R
ec
all,
o
r
s
en
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iti
v
it
y
in
b
in
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y
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s
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i
f
icat
io
n
,
in
d
icate
s
h
o
w
m
a
n
y
p
o
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it
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in
th
e
s
a
m
p
le
ar
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r
r
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tly
p
r
ed
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.
R
ec
all
is
c
o
m
p
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s
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o
f
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P
an
d
FN.
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ec
all
ca
n
b
e
o
b
tain
ed
f
r
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m
(
1
2
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as f
o
llo
w
s
[
4
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.
=
+
(
1
2
)
2
.
5
.
4
.
F1
-
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co
re
F1
-
s
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r
e
is
th
e
h
ar
m
o
n
ic
m
ea
n
o
f
p
r
ec
is
io
n
an
d
r
ec
all
[
4
]
.
I
n
o
th
er
w
o
r
d
s
,
f
1
-
s
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r
e
is
an
ef
f
ec
tiv
e
w
a
y
to
m
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s
u
r
e
t
h
e
ac
cu
r
ac
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o
f
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c
lass
i
f
icatio
n
m
et
h
o
d
b
y
u
s
i
n
g
th
e
m
ea
n
o
f
p
r
ec
is
i
o
n
an
d
r
ec
all
in
its
ca
lc
u
latio
n
[
3
]
.
F1
-
s
co
r
e
ca
n
b
e
ca
lcu
lated
as in
(
1
3
)
[
4
]
.
1
=
+
1
2
(
+
)
(
1
3
)
2
.
5
.
5
.
M
ea
n a
bs
o
lute
er
ro
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MA
E
is
u
s
ed
to
ev
al
u
ate
th
e
m
ea
s
u
r
e
b
et
w
ee
n
ac
t
u
al
a
n
d
p
r
ed
icted
v
alu
e
s
.
I
t i
n
d
icate
s
t
h
e
d
if
f
er
en
c
e
b
et
w
ee
n
th
e
tar
g
et
v
al
u
es
an
d
th
e
p
r
ed
icted
v
alu
es
[
2
6
]
.
MA
E
r
an
g
es
f
r
o
m
ze
r
o
to
in
f
in
i
t
y
,
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it
h
lo
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er
v
alu
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s
d
ef
in
i
n
g
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etter
ac
cu
r
ac
y
a
n
d
in
f
in
i
t
y
r
ep
r
ese
n
ti
n
g
th
e
m
a
x
i
m
u
m
er
r
o
r
in
p
r
ed
ictio
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r
a
n
k
i
n
g
s
[
6
]
.
MA
E
ca
n
b
e
co
m
p
u
ted
as i
n
(
1
4
)
.
=
∑
|
−
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=
1
(
1
4
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w
h
er
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̂
is
p
r
ed
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v
alu
e,
is
ac
tu
al
v
a
lu
e
s
,
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d
is
th
e
n
u
m
b
er
o
f
o
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s
er
v
atio
n
s
[
2
6
]
.
2
.
5
.
6
.
M
ea
n sq
ua
re
er
ro
r
MSE
is
t
h
e
s
q
u
ar
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o
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t
h
e
d
if
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ce
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et
w
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t
h
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t
u
al
v
al
u
e
an
d
t
h
e
e
v
alu
a
ted
v
al
u
e.
S
q
u
ar
in
g
is
d
o
n
e
to
eli
m
in
a
te
n
e
g
ati
v
e
v
al
u
es.
MSE
ca
n
b
e
ca
lcu
lated
as
in
(
1
5
)
[
2
6
]
.
=
∑
(
−
̂
)
2
=
1
(
1
5
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
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K
A
T
elec
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m
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o
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tr
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l
,
Vo
l.
23
,
No
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4
,
A
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20
25
:
1
020
-
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1
1026
2
.
5
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7
.
R
o
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m
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n s
qu
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ro
r
R
MSE
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etec
ts
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e
le
v
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er
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in
a
r
eg
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io
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m
o
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el
an
d
ex
a
m
in
e
s
th
e
s
ize
o
f
th
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er
r
o
r
r
elativ
e
to
th
e
s
ize
o
f
th
e
tar
g
et
v
alu
e
s
[
2
6
]
.
A
lo
w
er
R
MSE
v
a
lu
e
d
e
f
in
es
a
b
etter
p
r
ed
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n
ac
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r
ac
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o
f
th
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lt
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h
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ec
o
m
m
e
n
d
atio
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s
y
s
te
m
(
R
S)
[
6
]
.
R
MSE
is
ca
lcu
lated
as i
n
(
1
6
)
[
2
6
]
.
=
√
∑
(
−
̂
)
2
=
1
(
1
6
)
2
.
6
.
St
a
t
is
t
ica
l t
ests
Statis
t
ical
tes
ts
ar
e
u
s
ed
to
a
n
al
y
ze
d
ata
t
h
at
r
eq
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ir
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a
s
s
u
m
p
tio
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s
f
o
r
th
e
r
es
u
lt
s
to
b
e
v
a
lid
[
2
7
]
.
T
h
e
p
r
im
ar
y
p
u
r
p
o
s
e
o
f
s
tatis
tical
t
ests
is
to
ass
es
s
th
e
v
al
id
it
y
o
f
a
clai
m
o
r
h
y
p
o
t
h
esi
s
ab
o
u
t
a
m
o
d
el
o
r
p
ar
am
eter
v
alu
e
b
a
s
ed
o
n
th
e
o
b
s
er
v
ed
d
ata
[
2
8
]
.
I
n
th
is
s
t
u
d
y
,
s
tat
is
tic
al
tes
ts
ar
e
d
iv
id
ed
i
n
to
t
w
o
t
y
p
es
,
s
u
c
h
as
S
h
ap
ir
o
-
W
ilk
t
est a
n
d
T
-
T
est.
2
.
6
.
1
.
Sh
a
piro
-
w
ilk
t
est
Sh
ap
ir
o
-
W
il
k
test
is
o
n
e
o
f
t
h
e
m
o
s
t
i
m
p
o
r
tan
t
m
et
h
o
d
s
f
o
r
test
i
n
g
t
h
e
n
o
r
m
alit
y
a
s
s
u
m
p
tio
n
[
2
9
]
.
Sh
ap
ir
o
-
W
il
k
test
is
u
s
ed
to
d
eter
m
i
n
e
w
h
e
th
er
a
s
a
m
p
le
f
r
o
m
a
p
o
p
u
latio
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co
m
es f
r
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m
a
n
o
r
m
all
y
d
is
tr
ib
u
ted
p
o
p
u
latio
n
[
3
0
]
.
Sh
ap
ir
o
-
W
ilk
test
is
p
er
f
o
r
m
ed
as i
n
(
1
7
)
.
=
(
∑
(
)
=
1
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2
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(
−
̅
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=
1
2
(
1
7
)
w
h
er
e
(
)
ar
e
th
e
s
a
m
p
le
v
alu
e
s
o
r
d
er
e
d
f
r
o
m
th
e
s
m
a
lles
t
to
l
ar
g
est,
is
co
n
s
ta
n
t
o
b
tain
ed
f
r
o
m
th
e
m
ea
n
,
v
ar
ian
ce
,
a
n
d
co
v
ar
ia
n
ce
o
f
t
h
e
s
eq
u
e
n
ce
s
tat
is
tic
o
f
a
s
a
m
p
l
e
s
ize
f
r
o
m
a
n
o
r
m
al
d
i
s
tr
ib
u
ti
o
n
,
̅
is
t
h
e
s
a
m
p
l
e
m
ea
n
,
a
n
d
n
is
th
e
s
a
m
p
le
s
ize
[
3
1
]
.
2
.
6
.
2
.
T
-
t
est
T
-
test
is
a
s
tat
is
tical
test
u
s
ed
to
co
m
p
ar
e
th
e
m
ea
n
s
o
f
t
w
o
g
r
o
u
p
s
[
3
2
]
.
I
n
th
is
s
t
u
d
y
,
a
p
air
ed
t
-
test
is
u
s
ed
to
d
em
o
n
s
tr
ate
t
h
e
p
r
esen
ce
o
f
a
s
ig
n
i
f
ica
n
t
d
if
f
er
en
ce
in
p
er
f
o
r
m
a
n
ce
b
et
w
ee
n
t
h
e
co
m
p
ar
ed
m
o
d
els
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ased
o
n
ac
cu
r
ac
y
m
etr
ics
a
n
d
MA
E
.
T
h
e
p
air
ed
t
-
test
is
u
s
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l
w
h
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n
a
n
al
y
z
in
g
t
h
e
s
a
m
e
d
ata
s
et
m
ea
s
u
r
ed
u
n
d
er
t
w
o
d
if
f
er
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n
t
co
n
d
itio
n
s
,
th
e
m
ea
s
u
r
e
m
e
n
t
d
if
f
er
en
ce
s
o
n
th
e
s
a
m
e
s
u
b
j
ec
t
b
ef
o
r
e
an
d
af
ter
tr
ea
t
m
en
t,
o
r
th
e
d
if
f
er
e
n
ce
s
b
et
w
ee
n
t
w
o
t
r
ea
t
m
en
ts
g
iv
e
n
to
t
h
e
s
a
m
e
s
u
b
j
ec
t
[
3
3
]
.
T
h
e
ca
lcu
latio
n
o
f
t
h
e
p
air
ed
t
-
test
i
s
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er
f
o
r
m
ed
u
s
i
n
g
(
1
8
)
.
=
−
i
(
1
8
)
As
s
h
o
w
n
i
n
(
18
)
s
h
o
w
s
t
h
e
p
air
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d
if
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ce
eq
u
atio
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,
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th
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d
if
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ce
b
et
w
ee
n
th
e
co
r
r
esp
o
n
d
in
g
o
b
s
er
v
atio
n
s
f
r
o
m
t
w
o
s
a
m
p
les,
d
en
o
ted
b
y
an
d
i
,
w
h
er
e
r
ep
r
esen
ts
o
n
e
ele
m
en
t
i
n
ea
ch
p
air
o
f
o
b
s
er
v
atio
n
s
.
T
h
e
co
r
r
esp
o
n
d
in
g
p
air
ed
d
if
f
er
e
n
ce
s
ar
e
tr
ea
t
ed
as
a
v
ar
iab
le,
w
it
h
th
e
lo
g
ic
o
f
th
e
p
air
ed
t
-
test
an
d
th
e
o
n
e
-
s
a
m
p
le
t
-
te
s
t
b
ei
n
g
id
en
tical.
T
h
is
eq
u
atio
n
is
t
h
en
i
n
cl
u
d
ed
in
th
e
ca
lcu
la
tio
n
o
f
t
h
e
m
e
an
o
f
th
e
d
if
f
er
e
n
ce
an
d
t
h
e
s
ta
n
d
ar
d
d
e
v
iatio
n
o
f
th
e
d
i
f
f
er
en
ce
,
as s
h
o
w
n
b
y
(
1
9
)
an
d
(
2
0
)
r
esp
ec
ti
v
el
y
[
3
4
]
.
̅
=
∑
=
1
(
1
9
)
=
√
∑
(
−
̅
)
2
=
1
−
1
(
2
0
)
B
ased
o
n
th
e
ca
lcu
latio
n
s
u
s
i
n
g
(
1
9
)
an
d
(
2
0
)
,
̅
is
d
en
o
ted
as
th
e
m
ea
n
,
as th
e
s
tan
d
ar
d
d
ev
iatio
n
,
an
d
as
th
e
s
a
m
p
le
s
ize.
T
h
e
r
e
s
u
lt
s
o
f
t
h
e
ca
lc
u
latio
n
s
f
o
r
th
e
m
ea
n
a
n
d
s
ta
n
d
ar
d
d
ev
iatio
n
o
f
th
e
d
if
f
er
en
ce
s
ar
e
u
s
ed
f
o
r
th
e
t
-
s
tati
s
tic,
w
h
i
ch
in
v
o
lv
e
s
d
iv
id
i
n
g
t
h
e
m
ea
n
o
f
th
e
d
if
f
er
en
ce
(
̅
)
b
y
th
e
s
tan
d
ar
d
er
r
o
r
o
f
th
e
m
ea
n
o
f
th
e
d
i
f
f
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en
ce
,
d
en
o
te
d
as
√
.
T
h
e
ca
lcu
latio
n
o
f
th
e
t
-
s
tatis
t
ic
is
s
h
o
w
n
i
n
(
2
1
)
[
3
4
]
.
=
̅
√
⁄
(
2
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
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C
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m
p
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tr
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Gri
d
s
ea
r
ch
vs B
a
ye
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p
timiz
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tio
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fo
r
in
ten
s
ity
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co
r
in
g
cla
s
s
ifica
tio
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n
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…
(
K
elly
M
a
e
)
1027
3.
RE
SU
L
T
S AN
D
D
I
SCU
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I
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Evaluation Warning : The document was created with Spire.PDF for Python.
T
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(
K
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1029
T
ab
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