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[
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p
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im
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2
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1
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3
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[
5
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[
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5
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[
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6
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[
1
7
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[
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8
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335
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[
1
9
]
489
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[
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d
els
h
av
e
b
e
e
n
t
est
ed
[1
7
]
.
2.
M
E
T
H
O
D
R
ec
en
t
ad
v
an
ce
s
in
m
ac
h
in
e
lear
n
in
g
a
n
d
lear
n
in
g
an
aly
ti
cs
ca
n
n
o
w
im
p
r
o
v
e
th
e
f
o
r
e
ca
s
tin
g
o
f
lear
n
in
g
tim
e
i
n
co
u
r
s
es.
T
h
e
ad
o
p
tio
n
b
y
th
e
E
u
r
o
p
ea
n
C
o
m
m
is
s
io
n
o
f
s
tan
d
a
r
d
s
s
u
ch
as
C
MI
-
5
[
20
]
h
as
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
E
n
h
a
n
cin
g
lea
r
n
i
n
g
o
u
tco
mes in
s
ma
r
t e
d
u
ca
tio
n
:
A
s
u
p
ervis
ed
ma
ch
in
e
…
(
A
b
d
ella
h
B
a
k
h
o
u
yi)
4713
m
ad
e
it
ea
s
ier
to
d
ev
elo
p
m
o
r
e
s
o
p
h
is
ticated
p
r
ed
ictiv
e
m
o
d
els,
wh
ich
h
a
v
e
g
r
ea
tly
im
p
r
o
v
ed
t
h
e
ca
p
ac
ity
to
co
llect
an
d
e
v
alu
ate
a
wea
lth
o
f
d
ata
o
n
lear
n
e
r
-
to
-
lea
r
n
er
in
ter
ac
tio
n
s
an
d
lear
n
in
g
o
u
t
co
m
es.
C
o
m
b
in
in
g
m
ac
h
in
e
lear
n
in
g
an
d
C
MI
-
5
allo
ws
f
o
r
ac
c
u
r
ate
p
r
ed
ictio
n
o
f
h
o
w
lo
n
g
a
c
o
u
r
s
e
will
ta
k
e
to
c
o
m
p
lete,
an
d
p
r
o
v
id
es
teac
h
er
s
with
m
o
r
e
i
n
f
o
r
m
atio
n
ab
o
u
t
t
h
e
lear
n
i
n
g
r
ate
an
d
p
r
o
g
r
ess
o
f
th
eir
s
tu
d
en
ts
.
B
y
an
aly
s
in
g
h
is
to
r
ical
in
ter
ac
tio
n
d
ata,
th
e
s
e
p
r
ed
ictiv
e
m
o
d
els
ca
n
esti
m
ate
th
e
tim
e
it
will
tak
e
f
o
r
in
d
iv
id
u
al
lear
n
er
s
to
co
m
p
lete
s
p
ec
if
ic
m
o
d
u
les
o
r
co
u
r
s
es.
Pro
v
id
in
g
s
tu
d
en
ts
wi
th
r
ea
s
o
n
ab
le
e
x
p
ec
tatio
n
s
f
o
r
th
e
d
u
r
atio
n
o
f
th
e
co
u
r
s
e
ca
n
in
cr
ea
s
e
m
o
tiv
ati
o
n
a
n
d
e
n
g
ag
e
m
en
t,
f
o
s
ter
in
g
a
m
o
r
e
s
tim
u
latin
g
a
n
d
p
r
o
d
u
ctiv
e
lear
n
in
g
en
v
ir
o
n
m
en
t.
C
o
n
s
eq
u
e
n
tly
,
m
ac
h
in
e
lear
n
in
g
an
d
th
e
in
te
g
r
atio
n
o
f
C
MI
-
5
ar
e
n
o
t e
x
clu
s
iv
e.
Mo
o
d
le
is
th
e
m
ain
p
latf
o
r
m
f
o
r
u
s
er
a
d
m
in
is
tr
atio
n
,
co
u
r
s
e
d
eliv
er
y
a
n
d
co
m
m
u
n
icatio
n
b
etwe
en
s
tu
d
en
ts
an
d
co
u
r
s
e
m
ater
ials
.
A
lear
n
in
g
r
ec
o
r
d
s
to
r
e
(
L
R
S)
is
b
u
ilt
in
to
Mo
o
d
le
to
en
h
an
ce
its
em
b
ed
d
ed
ca
p
ab
ilit
ies
an
d
r
ec
o
r
d
a
v
ar
iety
o
f
lear
n
in
g
ac
tiv
ities
th
at
ar
e
p
er
f
o
r
m
ed
o
n
th
e
p
latf
o
r
m
[
2
1
]
.
Data
o
n
lear
n
in
g
e
x
p
er
ien
ce
s
ca
n
b
e
m
an
ag
ed
,
s
to
r
ed
a
n
d
a
n
aly
ze
d
in
a
s
p
ec
ialized
s
to
r
ag
e
f
ac
ili
ty
ca
lled
L
R
S
[
22
]
.
T
h
e
L
R
S
g
u
ar
an
tees
f
u
ll
co
m
p
atib
ilit
y
an
d
in
ter
o
p
er
ab
ilit
y
with
a
wid
e
r
an
g
e
o
f
d
ig
ital
teac
h
in
g
to
o
ls
an
d
p
latf
o
r
m
s
,
as
it
h
as
b
ee
n
d
ev
elo
p
ed
in
ac
co
r
d
a
n
ce
with
th
e
C
MI
-
5
s
p
ec
if
icatio
n
.
T
h
e
L
R
S
co
m
b
in
es
d
ata
s
tr
ea
m
s
an
d
p
r
o
v
id
es
teac
h
er
s
an
d
ad
m
in
is
tr
ato
r
s
with
co
m
p
r
eh
en
s
iv
e
i
n
s
ig
h
t
in
to
s
tu
d
en
t
p
er
f
o
r
m
a
n
ce
,
p
r
o
g
r
ess
an
d
b
e
h
av
io
r
th
r
o
u
g
h
s
ea
m
less
in
teg
r
atio
n
with
th
e
L
R
S,
wh
ich
co
n
n
ec
ts
th
e
Mo
o
d
le
an
d
ass
ig
n
ab
l
e
u
n
its
(
AUs).
Ass
ig
n
ab
le
u
n
its
ar
e
th
o
s
e
th
at
r
e
p
r
esen
t
d
if
f
e
r
en
t
co
m
p
o
n
en
ts
o
f
lear
n
in
g
a
ctiv
ities
o
r
co
n
ten
t,
wh
ich
ar
e
n
ec
ess
ar
y
f
o
r
t
h
e
L
R
S
to
f
u
n
ctio
n
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
f
o
r
co
llectin
g
lea
r
n
in
g
ex
p
er
ien
ce
d
ata
b
y
u
s
in
g
th
e
in
te
g
r
atio
n
o
f
C
MI
-
5
s
p
ec
if
icatio
n
s
to
to
e
n
ab
le
th
e
ac
c
u
r
ate
p
r
ed
ictio
n
o
f
tim
e
to
c
o
u
r
s
e
co
m
p
letio
n
is
s
h
o
wn
i
n
Fig
u
r
e
1
.
T
h
e
p
r
ed
ictiv
e
a
n
aly
tics
co
m
p
o
n
en
t
o
f
th
is
s
tu
d
y
is
r
ep
r
esen
ted
b
y
t
h
e
lear
n
in
g
r
ec
o
r
d
co
n
s
u
m
er
(
L
R
C
)
,
wh
ich
u
s
es
s
u
p
er
v
is
e
d
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
e
s
to
p
r
ed
ict
h
o
w
lo
n
g
it
will
tak
e
to
co
m
p
lete
a
co
u
r
s
e
b
ased
o
n
th
e
s
tr
u
ctu
r
ed
d
ata
s
to
r
ed
in
T
h
e
L
ea
r
n
in
g
R
ec
o
r
d
.
Fo
r
th
e
ac
cu
r
ate
p
r
ed
i
ctio
n
o
f
th
e
tim
e
to
co
u
r
s
e
co
m
p
letio
n
,
o
u
r
m
eth
o
d
o
lo
g
y
u
s
es
a
s
tr
u
ctu
r
ed
p
r
o
c
ess
wh
ich
in
clu
d
es
d
ata
co
llec
tio
n
,
p
r
e
-
tr
ea
tm
en
t,
an
aly
s
is
an
d
m
o
d
ellin
g
.
T
o
e
s
tim
ate
th
e
co
u
r
s
e
c
o
m
p
letio
n
tim
e
(
C
C
T
)
o
f
in
d
iv
id
u
al
l
ea
r
n
er
s
,
s
u
p
e
r
v
is
ed
lear
n
in
g
alg
o
r
ith
m
s
s
u
ch
as
R
F,
g
r
ad
ien
t
b
o
o
s
tin
g
(
GB
)
,
SVM,
DT
an
d
L
R
ar
e
u
s
ed
in
th
e
p
r
ed
ictiv
e
m
o
d
el,
as illu
s
tr
ated
in
Fig
u
r
e
2
.
W
e
p
r
o
p
o
s
e
a
m
eth
o
d
ical
an
d
s
tr
u
ctu
r
e
d
ap
p
r
o
ac
h
to
co
ll
ec
t,
p
r
ep
a
r
e,
a
n
aly
ze
a
n
d
m
o
d
el
lear
n
er
d
ata
f
o
r
ac
c
u
r
ate
C
C
T
p
r
ed
ict
io
n
.
Fig
u
r
e
2
s
h
o
ws
th
e
d
if
f
er
en
t
s
tag
es
o
f
th
e
p
r
e
d
ictiv
e
a
n
aly
tics
f
r
am
ewo
r
k
.
Fo
r
th
e
esti
m
atio
n
o
f
C
C
T
f
o
r
in
d
iv
id
u
al
lear
n
e
r
s
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
m
o
d
els
wer
e
u
s
ed
;
DT
,
GB
,
R
F,
SVM
,
an
d
L
R
.
T
h
e
m
eth
o
d
o
lo
g
y
is
co
m
p
o
s
ed
o
f
a
n
u
m
b
er
o
f
k
ey
s
tep
s
,
as illu
s
tr
ated
in
Fig
u
r
e
2
.
F
i
g
u
r
e
1
.
P
r
o
p
o
s
e
d
m
o
d
e
l
f
o
r
c
o
l
l
e
c
t
i
n
g
l
e
a
r
n
i
n
g
e
x
p
e
r
i
e
n
c
e
d
a
t
a
t
h
r
o
u
g
h
t
h
e
i
n
t
e
g
r
a
t
i
o
n
o
f
C
M
I
-
5
s
p
e
c
i
f
i
c
a
t
i
o
n
s
Fig
u
r
e
2
.
Pro
p
o
s
ed
s
u
p
e
r
v
is
ed
m
ac
h
in
e
lear
n
i
n
g
p
r
ed
ictiv
e
a
n
aly
tics
m
o
d
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
4
,
No
.
6
,
Dec
em
b
er
2
0
2
5
:
4
7
1
1
-
4
7
2
1
4714
2
.
1
.
I
dentif
y
ing
da
t
a
s
o
urce
s
Data
co
llectio
n
is
th
e
f
ir
s
t
s
tep
f
r
o
m
t
h
e
L
R
S,
wh
ich
is
p
r
o
v
id
ed
b
y
th
e
lear
n
in
g
r
ec
o
r
d
p
r
o
v
id
e
r
(
L
R
P
)
an
d
is
lin
k
ed
to
th
e
l
ea
r
n
in
g
m
an
a
g
em
en
t
s
y
s
tem
(
L
MS)
wh
ich
is
co
m
p
lian
t
with
C
MI
-
5
.
T
h
ese
s
y
s
tem
s
r
ec
o
r
d
m
an
y
d
ata
p
o
in
ts
,
s
u
ch
as
s
tu
d
en
t
d
em
o
g
r
ap
h
ics,
test
p
er
f
o
r
m
a
n
ce
,
m
o
d
u
le
p
r
o
g
r
ess
,
an
d
s
tu
d
en
t'
s
u
n
d
er
s
tan
d
in
g
o
f
th
e
m
ater
ial.
I
n
o
r
d
er
to
en
s
u
r
e
th
e
u
n
iv
e
r
s
ality
an
d
r
o
b
u
s
tn
es
s
o
f
th
e
p
r
e
d
ictio
n
m
o
d
el,
th
e
C
C
T
d
ata
s
et
s
h
o
u
l
d
co
v
er
a
wid
e
r
a
n
g
e
o
f
co
u
r
s
es a
n
d
lear
n
er
s
.
2
.2
.
Da
t
a
prepa
ra
t
i
o
n a
nd
pre
-
pro
ce
s
s
ing
T
o
en
s
u
r
e
th
e
q
u
ality
an
d
an
aly
tical
p
o
ten
tial
o
f
th
e
d
ata,
th
ey
u
n
d
e
r
g
o
a
th
o
r
o
u
g
h
p
r
e
-
p
r
o
ce
s
s
in
g
p
h
ase
af
ter
co
llectio
n
.
T
h
is
s
tep
in
v
o
lv
es
th
e
p
r
o
ce
s
s
in
g
o
f
m
is
s
in
g
v
alu
es,
en
c
o
d
in
g
o
f
c
ateg
o
r
ical
v
a
r
iab
les
an
d
th
e
p
r
o
ce
s
s
in
g
o
f
am
b
i
g
u
o
u
s
d
ata,
n
o
r
m
aliza
tio
n
o
f
n
u
m
er
ical
ch
ar
ac
ter
is
tics
,
an
d
d
etec
tio
n
an
d
tr
ea
tm
en
t
o
f
p
o
s
s
ib
le
o
u
tlier
s
.
Fo
r
th
e
p
r
ed
ictio
n
o
f
CCT
,
m
ac
h
in
e
lear
n
in
g
m
o
d
els
ar
e
tr
ain
ed
u
s
in
g
th
e
f
ea
tu
r
es tak
en
f
r
o
m
th
e
C
MI
-
5
d
ec
lar
atio
n
.
T
r
an
s
f
o
r
m
in
g
C
MI
-
5
co
m
m
a
n
d
s
in
to
m
ac
h
in
e
lear
n
in
g
-
r
ea
d
y
d
ata
s
ets
r
eq
u
ir
es
p
ar
s
in
g
J
SON
s
tr
u
ctu
r
es
to
ex
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f
d
ata
s
et
s
tr
u
ctu
r
e
a
n
d
p
r
o
p
e
r
ties
,
e
x
p
lo
r
atio
n
d
a
ta
an
aly
s
is
(
E
DA)
was
p
er
f
o
r
m
ed
p
r
io
r
to
r
u
n
n
in
g
p
r
ed
ictiv
e
m
o
d
els.
T
h
is
an
aly
tical
p
h
ase
was
n
ec
ess
ar
y
to
id
en
tify
s
tatis
t
ical
tr
en
d
s
,
p
o
s
s
ib
le
o
u
t
lier
s
an
d
th
e
f
u
n
d
a
m
en
tal
co
r
r
elatio
n
s
b
etwe
en
in
p
u
t
p
r
o
p
e
r
ties
an
d
th
e
tar
g
et
v
ar
iab
le,
C
C
T
.
Var
io
u
s
v
is
u
aliza
tio
n
to
o
ls
,
s
u
ch
as
ch
ar
ts
,
s
u
m
m
ar
ies,
co
r
r
elatio
n
ch
ar
t
s
an
d
h
is
to
g
r
am
s
,
h
av
e
b
ee
n
u
s
ed
to
ass
is
t
in
th
i
s
p
r
o
ce
s
s
.
T
h
ese
m
eth
o
d
s
h
av
e
allo
wed
th
e
ev
alu
atio
n
o
f
v
a
r
iab
le
d
is
tr
ib
u
tio
n
s
an
d
th
e
d
etec
tio
n
o
f
co
r
r
elatio
n
s
wh
ich
g
u
id
e
th
e
s
elec
tio
n
o
f
f
ea
tu
r
es a
n
d
th
e
tr
ain
in
g
o
f
m
o
d
els
[2
4
]
.
T
h
e
d
ataset
an
aly
ze
d
co
n
s
is
ted
o
f
8
,
6
6
5
lear
n
er
in
ter
ac
tio
n
r
ep
o
r
ts
co
llected
b
etwe
en
th
e
y
ea
r
s
2
0
1
9
an
d
2
0
2
4
.
B
ased
o
n
a
C
MI
-
5
co
m
p
lian
t
lear
n
in
g
e
n
v
ir
o
n
m
e
n
t,
th
ese
ass
e
s
s
m
en
ts
p
r
o
v
id
ed
a
d
ee
p
in
s
ig
h
t
in
to
th
e
in
ter
ac
tio
n
p
atter
n
s
an
d
le
ar
n
er
en
g
ag
em
e
n
t
b
eh
av
io
r
.
Sig
n
if
ican
t
p
atter
n
s
,
r
ep
r
esen
tin
g
d
if
f
e
r
en
t
lev
els
o
f
co
m
m
itm
en
t
an
d
d
e
v
elo
p
m
e
n
t
o
f
lear
n
er
s
d
u
r
in
g
th
e
lear
n
in
g
p
r
o
ce
s
s
,
h
av
e
b
ee
n
id
en
ti
f
ied
b
y
ex
am
i
n
in
g
s
p
ec
if
ic
ty
p
es
o
f
in
te
r
ac
tio
n
.
Ob
s
er
v
ed
in
6
7
0
in
ter
v
iews,
t
h
e
p
lay
ed
ac
tiv
ity
was
ass
o
ciate
d
with
an
av
e
r
ag
e
o
f
2
0
-
7
0
m
in
u
tes
an
d
2
-
4
7
in
t
er
ac
tio
n
s
p
er
s
ess
io
n
,
wh
ich
in
d
icate
s
a
r
eg
u
lar
an
d
s
ig
n
if
ic
an
t
in
v
o
lv
em
e
n
t
in
th
e
m
ater
ial
o
f
th
e
c
o
u
r
s
e.
On
th
e
o
th
e
r
h
a
n
d
,
t
h
e
'
Pau
s
e
'
ac
tio
n
was
u
s
ed
in
6
7
7
co
m
m
an
d
s
,
with
an
a
v
er
ag
e
s
ess
io
n
d
u
r
atio
n
o
f
6
:8
7
m
i
n
u
tes
an
d
n
o
ad
d
itio
n
al
in
ter
ac
ti
o
n
.
T
h
is
s
u
g
g
ests
th
at
lear
n
er
s
m
ay
h
av
e
p
au
s
ed
f
o
r
a
m
o
m
en
t,
m
o
s
t lik
ely
to
r
ef
lect,
to
tak
e
n
o
tes,
o
r
to
te
m
p
o
r
ar
ily
s
h
if
t t
h
eir
atten
tio
n
.
T
h
e
in
ter
ac
tio
n
“T
e
r
m
in
ated
”
was
d
etec
ted
in
2
1
8
r
ep
o
r
ts
.
As
s
h
o
wn
in
Fig
u
r
e
4
,
t
h
is
r
ep
r
esen
ts
a
s
u
s
tain
ed
ef
f
o
r
t
p
r
io
r
to
d
is
en
g
ag
em
en
t,
with
an
av
er
ag
e
in
ter
ac
tio
n
d
u
r
atio
n
o
f
3
.
2
0
in
Fig
u
r
e
4
(
a
)
an
d
an
av
er
ag
e
d
u
r
atio
n
o
f
4
2
.
2
2
m
i
n
u
tes
in
Fig
u
r
e
4
(
b)
.
T
h
is
b
e
h
av
io
r
m
ay
b
e
an
in
d
icatio
n
o
f
co
n
ten
t
d
if
f
icu
lty
,
co
g
n
itiv
e
o
v
e
r
lo
ad
,
o
r
ex
ter
n
al
d
is
tr
ac
tio
n
s
th
at
ca
u
s
ed
th
e
lear
n
in
g
s
ess
io
n
to
b
e
in
ter
r
u
p
ted
p
r
em
atu
r
ely
.
Fin
ally
,
1
6
8
s
tatem
en
ts
th
at
i
n
clu
d
ed
th
e
“Co
m
p
leted
”
ac
ti
o
n
s
h
o
we
d
an
av
er
a
g
e
e
n
g
ag
e
m
en
t
tim
e
o
f
3
6
.
2
3
m
in
u
tes
with
3
.
3
6
in
ter
ac
tio
n
s
,
in
d
icatin
g
a
h
ig
h
lev
el
o
f
p
er
s
is
ten
ce
an
d
co
m
m
itm
en
t
b
y
lear
n
er
s
to
co
m
p
letin
g
task
s
.
T
h
ese
d
escr
ip
tiv
e
in
s
ig
h
ts
,
illu
s
tr
ated
g
r
a
p
h
ically
in
Fig
u
r
e
4
,
h
elp
to
c
r
ea
te
m
o
r
e
ac
cu
r
ate
an
d
ef
f
ec
tiv
e
p
r
e
d
ictiv
e
m
o
d
els
f
o
r
p
r
ed
ictin
g
co
u
r
s
e
co
m
p
letio
n
an
d
to
ad
v
an
ce
a
m
o
r
e
n
u
an
ce
d
u
n
d
er
s
tan
d
i
n
g
o
f
lear
n
er
b
eh
a
v
io
r
.
2
.
4
.
Da
t
a
prepa
ra
t
i
o
n a
nd
pre
-
pro
ce
s
s
ing
T
h
e
s
tu
d
y
ev
alu
ates
s
ix
m
ac
h
in
e
lear
n
in
g
m
o
d
els
f
o
r
t
h
e
es
tim
atio
n
o
f
s
tu
d
en
t
lear
n
in
g
c
o
m
p
letio
n
r
ates: L
R
,
g
r
ad
ien
t e
n
h
an
ce
m
e
n
t (
GB
)
,
s
u
p
p
o
r
t v
ec
t
o
r
r
eg
r
ess
io
n
(
SVR
)
,
R
F,
DT
an
d
ANN
.
T
h
e
ch
o
ice
o
f
th
e
m
o
d
el
to
o
k
in
to
ac
c
o
u
n
t
th
e
r
ea
d
ab
ilit
y
,
co
m
p
lex
ity
o
f
th
e
t
ask
an
d
th
e
c
h
ar
ac
ter
is
tics
o
f
t
h
e
d
ataWeKA
u
s
ed
a
f
iv
e
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
t
o
d
iv
id
e
th
e
d
ataset
in
to
2
0
p
er
ce
n
t
test
in
g
a
n
d
8
0
p
er
ce
n
t
tr
ain
in
g
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
m
o
d
el
was e
v
alu
ated
b
y
u
s
in
g
t
h
e
m
etr
ic
R
-
s
q
u
ar
ed
(
R
²)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
4
,
No
.
6
,
Dec
em
b
er
2
0
2
5
:
4
7
1
1
-
4
7
2
1
4716
(
a)
(
b
)
Fig
u
r
e
4
.
Stu
d
e
n
t in
ter
ac
tio
n
s
an
d
p
r
o
g
r
ess
f
o
r
C
C
T
f
r
o
m
2
0
1
9
to
2
0
2
4
(
d
ataset: 8
,
6
6
5
s
tatem
en
ts
)
:
(
a)
av
er
a
g
e
n
u
m
b
er
o
f
in
ter
ac
t
io
n
s
b
y
ac
tiv
ity
v
er
b
a
n
d
(
b
)
a
v
er
ag
e
tim
e
s
p
en
t
b
y
ac
tiv
ity
v
er
b
.
2
.
5
.
M
o
del
t
ra
ini
ng
a
nd
ev
a
l
ua
t
io
n
T
o
a
s
s
e
s
s
t
h
e
p
r
e
d
i
c
t
i
v
e
p
o
w
e
r
o
f
t
h
e
a
l
g
o
r
i
t
h
m
s
s
e
l
e
c
t
e
d
,
t
h
e
y
w
e
r
e
t
r
a
i
n
e
d
a
n
d
e
v
a
l
u
a
t
e
d
o
n
d
i
f
f
e
r
e
n
t
s
u
b
s
e
t
s
o
f
t
h
e
d
a
t
a
s
e
t
.
T
h
e
r
e
g
r
e
s
s
i
o
n
m
e
a
s
u
r
e
s
s
u
c
h
a
s
t
h
e
r
o
o
t
m
e
a
n
s
q
u
a
r
e
(
s
y
m
m
e
t
r
i
c
)
a
n
d
t
h
e
m
e
a
n
a
b
s
o
l
u
t
e
e
r
r
o
r
(
M
A
E
)
.
T
h
e
e
v
a
l
u
a
t
i
o
n
u
s
e
d
e
x
p
l
a
i
n
e
d
v
a
r
i
a
n
c
e
s
c
o
r
e
(
E
V
S
)
,
r
o
o
t
m
e
a
n
s
q
u
a
r
e
d
e
r
r
o
r
(
R
M
S
E
)
,
r
e
l
a
t
i
v
e
a
b
s
o
l
u
t
e
e
r
r
o
r
(
R
A
E
)
,
a
n
d
m
e
d
i
a
n
a
b
s
o
l
u
t
e
e
r
r
o
r
(
M
e
d
A
E
)
a
s
m
e
a
s
u
r
e
s
o
f
t
h
e
e
x
p
l
a
n
a
t
o
r
y
v
a
r
i
a
b
i
l
i
t
y
[2
5
]
.
A
k
-
f
o
l
d
c
r
o
s
s
v
a
l
i
d
a
t
i
o
n
h
a
s
b
e
e
n
u
s
e
d
t
o
i
m
p
r
o
v
e
r
e
s
i
s
t
a
n
c
e
a
n
d
r
e
d
u
c
e
o
v
e
r
f
i
t
t
i
n
g
.
E
V
S
c
a
l
c
u
l
a
t
e
s
t
h
e
p
e
r
c
e
n
t
o
f
t
h
e
d
e
v
i
a
t
i
o
n
e
x
p
l
a
i
n
e
d
b
y
t
h
e
m
o
d
e
l
,
M
e
d
A
e
c
a
l
c
u
l
a
t
e
s
t
h
e
a
v
e
r
a
g
e
d
e
v
i
a
t
i
o
n
b
e
t
w
e
e
n
e
x
p
e
c
t
e
d
a
n
d
o
b
s
e
r
v
e
d
v
a
l
u
e
s
,
a
n
d
R
A
E
q
u
a
n
t
i
f
i
e
s
t
h
e
e
r
r
o
r
i
n
r
e
l
a
t
i
o
n
t
o
t
h
e
m
e
a
n
o
f
t
h
e
o
b
s
e
r
v
e
d
v
a
l
u
e
s
[2
6
]
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
r
eg
r
ess
io
n
an
aly
s
is
,
it
is
im
p
o
r
tan
t
t
o
co
m
p
ar
e
a
n
d
c
o
n
tr
ast
d
if
f
er
en
t
m
o
d
els
to
u
n
d
e
r
s
tan
d
th
eir
g
en
er
aliza
b
ilit
y
an
d
p
r
e
d
ictio
n
ab
ilit
y
in
d
if
f
er
e
n
t
d
ata
s
itu
atio
n
s
[2
7
]
.
T
h
e
g
o
al
was
to
p
r
ed
ict
C
C
T
u
s
in
g
lear
n
er
in
ter
ac
tio
n
d
ata
th
at
m
et
C
MI
-
5
r
e
q
u
ir
em
en
ts
.
Us
in
g
lear
n
e
r
in
ter
ac
tio
n
d
ata
th
at
m
et
th
e
C
MI
-
5
r
eq
u
ir
em
e
n
ts
,
s
ix
m
ac
h
in
e
lea
r
n
in
g
tec
h
n
iq
u
es
wer
e
u
s
ed
to
p
r
ed
ict
t
h
e
tim
e
to
co
m
p
letio
n
o
f
th
e
c
o
u
r
s
e:
R
F,
DT
,
L
R
,
SVR
,
ANN
,
an
d
g
r
ad
ien
t b
o
o
s
tin
g
r
eg
r
ess
io
n
(
GB
R
)
.
T
h
e
R
²
m
etr
ic
was
u
s
ed
to
co
m
p
ar
e
ex
p
ec
ted
C
C
T
v
alu
es
with
ac
tu
al
r
esu
lts
to
ass
es
s
th
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eir
t
en
d
en
cy
t
o
o
v
er
f
it,
th
ey
s
h
o
u
ld
b
e
ap
p
lied
with
ca
u
tio
n
in
an
e
n
s
em
b
le
ap
p
r
o
ac
h
.
Ultim
ately
,
a
d
ee
p
e
r
u
n
d
er
s
tan
d
in
g
o
f
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
m
o
d
el,
s
u
p
p
o
r
ted
b
y
a
th
o
r
o
u
g
h
a
n
aly
s
is
o
f
m
etr
ics
an
d
r
ig
o
r
o
u
s
v
alid
atio
n
,
is
n
ec
ess
ar
y
to
m
a
k
e
a
wis
e
ch
o
ice
o
f
m
o
d
els an
d
to
im
p
lem
en
t t
h
e
m
ef
f
ec
tiv
ely
in
b
o
th
ap
p
lied
a
n
d
r
esear
ch
c
o
n
tex
ts
.
T
h
e
r
esu
lts
o
f
th
e
ev
alu
atio
n
o
f
th
e
p
r
e
d
ictio
n
m
o
d
els
ar
e
s
h
o
wn
in
Fig
u
r
e
5
.
A
p
er
f
o
r
m
a
n
ce
co
m
p
ar
is
o
n
b
ased
o
n
MA
E
,
MSE
,
an
d
R
MSE
is
p
r
esen
te
d
in
Fig
u
r
e
5
(
a)
,
h
ig
h
lig
h
tin
g
th
e
ac
cu
r
ac
y
o
f
th
e
p
r
ed
ictio
n
s
o
f
ea
ch
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
.
T
h
e
R
²
s
co
r
e
o
f
ea
ch
m
o
d
el
is
s
h
o
wn
in
Fig
u
r
e
5
(
b
)
to
s
h
o
w
h
o
w
well
ea
ch
m
o
d
el
ex
p
lain
s
th
e
ch
an
g
e
in
th
e
C
C
T
.
(
a)
(
b
)
Fig
u
r
e
5
.
Per
f
o
r
m
an
c
e
ev
alu
at
io
n
o
f
t
h
e
p
r
e
d
ictiv
e
an
aly
tics
m
o
d
els f
o
r
C
C
T
:
(
a)
c
o
m
p
ar
is
o
n
o
f
MA
E
,
MSE
,
an
d
R
MSE
s
co
r
es f
o
r
ea
ch
m
o
d
el,
an
d
(
b
)
R
²
s
co
r
e
r
ates o
f
t
h
e
C
C
T
p
r
ed
ictio
n
m
o
d
els
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
4
,
No
.
6
,
Dec
em
b
er
2
0
2
5
:
4
7
1
1
-
4
7
2
1
4718
T
h
e
ab
ilit
y
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
els
to
p
r
ed
ict
is
d
ep
en
d
en
t
o
n
h
o
w
well
th
ey
p
e
r
f
o
r
m
,
m
ea
s
u
r
ed
b
y
m
etr
ics
s
u
ch
as
R
MSE
.
T
h
e
co
n
s
is
ten
cy
an
d
r
eliab
ilit
y
o
f
th
e
m
o
d
el
p
r
ed
ictio
n
s
ar
e
r
e
v
ea
led
b
y
lo
o
k
in
g
a
t
th
e
R
MSE
s
co
r
e
f
o
r
ea
ch
f
o
ld
an
d
th
e
R
MSE
av
er
ag
e
f
o
r
th
e
cr
o
s
s
v
alid
atio
n
,
wh
ich
d
iv
i
d
es
th
e
d
ataset
in
to
s
ev
er
al
tr
ain
in
g
an
d
test
in
g
f
o
ld
s
[
2
8
]
.
T
h
e
Dec
is
io
n
T
r
ee
R
eg
r
ess
o
r
s
u
cc
ess
f
u
lly
ca
p
tu
r
es
th
e
u
n
d
er
l
y
in
g
d
ata
p
atter
n
s
,
as
s
h
o
wn
b
y
its
co
n
s
is
ten
tly
lo
w
R
M
SE
s
co
r
e
ac
r
o
s
s
all
f
o
ld
s
.
T
h
is
co
n
clu
s
io
n
is
f
u
r
th
er
s
u
p
p
o
r
ted
b
y
th
e
R
MSE
av
er
ag
e
cr
o
s
s
-
v
alid
atio
n
,
wh
ich
s
h
o
ws
co
n
s
is
ten
t
an
d
r
eliab
le
p
er
f
o
r
m
a
n
ce
ac
r
o
s
s
d
if
f
er
en
t
s
u
b
s
ets
o
f
d
ata.
T
h
is
r
o
b
u
s
tn
ess
ca
n
b
e
ex
p
lain
ed
b
y
th
e
in
h
er
en
t
s
im
p
licity
an
d
ad
a
p
tab
ilit
y
o
f
DT
m
o
d
els,
wh
ich
allo
w
th
em
to
w
o
r
k
wel
l w
ith
d
if
f
er
en
t
d
ata
d
is
tr
ib
u
tio
n
s
an
d
r
elatio
n
s
h
ip
s
.
Ho
wev
er
,
as
s
h
o
wn
b
y
th
e
r
e
lativ
ely
h
ig
h
e
r
R
MSE
s
co
r
e
f
o
r
f
o
ld
1
co
m
p
ar
ed
to
o
t
h
er
f
o
ld
s
,
th
e
ANN
m
o
d
el
s
h
o
ws
g
r
ea
ter
v
ar
iab
ilit
y
in
p
er
f
o
r
m
an
ce
ac
r
o
s
s
f
o
ld
s
.
I
ts
h
ig
h
er
av
er
ag
e
R
MSE
af
ter
cr
o
s
s
-
v
alid
atio
n
r
ef
lects
less
r
eliab
le
p
r
ed
ictiv
e
p
er
f
o
r
m
an
ce
.
T
h
is
v
ar
iab
ilit
y
m
ay
b
e
d
u
e
to
th
e
co
m
p
lex
ity
o
f
n
eu
r
al
n
etwo
r
k
s
,
wh
ic
h
ar
e
m
o
r
e
s
en
s
itiv
e
to
c
h
an
g
es
in
tr
a
in
in
g
d
ata
d
u
e
to
th
ei
r
m
an
y
p
ar
am
eter
s
an
d
th
e
r
is
k
o
f
o
v
er
f
itti
n
g
[2
9
]
.
Alth
o
u
g
h
th
e
g
r
ad
ien
t
en
h
a
n
ce
m
en
t
m
o
d
el
s
h
o
ws
a
lo
w
m
ea
n
R
MSE
f
o
r
cr
o
s
s
-
v
alid
atio
n
,
its
R
MSE
s
co
r
e
i
n
f
o
ld
1
is
s
lig
h
tly
h
ig
h
er
th
an
th
e
R
MSE
s
co
r
e
in
th
e
s
u
b
s
eq
u
en
t
f
o
ld
s
.
T
h
e
iter
ativ
e
n
atu
r
e
o
f
g
r
ad
ien
t
r
ei
n
f
o
r
ce
m
e
n
t
a
n
d
its
s
en
s
itiv
ity
to
th
e
o
r
d
er
o
f
th
e
l
o
w
-
p
er
f
o
r
m
in
g
lear
n
er
s
m
a
y
co
n
tr
ib
u
te
t
o
th
is
in
itial
v
ar
iab
ilit
y
in
p
er
f
o
r
m
an
ce
.
Ho
wev
er
,
th
e
lo
w
m
ea
n
R
MSE
o
f
th
e
m
o
d
el
s
u
g
g
ests
th
at
it h
as g
o
o
d
o
v
er
all
p
r
ed
ictiv
e
p
o
wer
in
LR
an
d
b
o
t
h
.
T
o
ass
ess
th
e
co
n
s
is
ten
cy
an
d
r
o
b
u
s
tn
ess
o
f
m
o
d
els,
we
u
s
e
d
5
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
a
n
d
t
esti
n
g
th
e
R
MSE
ac
r
o
s
s
all
f
o
ld
s
.
T
h
e
r
esu
lts
o
f
th
e
cr
o
s
s
-
v
alid
atio
n
o
f
ea
ch
m
o
d
el
ar
e
s
h
o
wn
in
F
ig
u
r
e
6
,
wh
ich
also
s
h
o
ws
th
e
co
r
r
esp
o
n
d
in
g
m
ea
n
R
MSE
an
d
R
MSE
o
b
tain
e
d
at
ea
ch
o
f
t
h
e
two
f
o
l
d
r
ates.
T
h
is
v
is
u
aliza
tio
n
allo
ws a
co
m
p
ar
ativ
e
ass
ess
m
en
t o
f
m
o
d
el
s
tab
ilit
y
an
d
g
en
er
alis
atio
n
p
er
f
o
r
m
an
ce
ac
r
o
s
s
d
ataset
s
u
b
s
ets.
T
h
e
SVM
m
o
d
el
h
as
a
h
ig
h
er
av
er
ag
e
R
MSE
s
co
r
e
f
o
r
cr
o
s
s
-
v
alid
atio
n
,
b
u
t
a
r
elativ
el
y
lo
w
R
MSE
s
co
r
e
f
o
r
all
f
o
ld
s
,
co
m
p
ar
ed
to
o
th
e
r
m
o
d
els.
Du
e
to
th
e
s
en
s
itiv
ity
o
f
SVMs
to
th
e
s
elec
tio
n
o
f
k
er
n
el
an
d
r
eg
u
lar
is
atio
n
p
a
r
am
eter
s
,
th
is
d
if
f
er
en
ce
im
p
lies
a
ce
r
tain
v
ar
iatio
n
in
p
e
r
f
o
r
m
an
ce
b
etwe
en
f
o
ld
s
.
Ad
ju
s
tin
g
th
ese
p
ar
am
eter
s
m
a
y
h
el
p
t
o
in
cr
ea
s
e
m
o
d
el
c
o
h
er
e
n
ce
an
d
r
e
d
u
ce
v
ar
iab
ilit
y
o
f
p
er
f
o
r
m
an
ce
b
etwe
en
d
if
f
er
en
t su
b
s
ets o
f
d
ata.
I
n
s
u
m
m
ar
y
,
all
m
o
d
els s
h
o
w
p
r
e
d
ictiv
e
ca
p
ab
ilit
y
,
b
u
t c
o
n
s
is
ten
cy
an
d
r
eliab
ilit
y
o
f
th
eir
p
er
f
o
r
m
a
n
ce
v
ar
ies
ac
c
o
r
d
in
g
t
o
th
e
d
ata
s
u
b
s
et.
I
t
is
n
ec
ess
ar
y
to
u
n
d
er
s
tan
d
t
h
ese
d
etails
in
o
r
d
e
r
to
s
elec
t
th
e
b
est
m
o
d
el
f
o
r
a
s
p
e
cif
ic
task
an
d
t
o
en
s
u
r
e
ac
cu
r
a
te
an
d
c
o
n
s
is
ten
t
p
r
ed
ictio
n
s
i
n
th
e
a
p
p
licatio
n
o
f
th
e
p
r
o
ce
d
u
r
e.
Fig
u
r
e
6
.
Vis
u
alizin
g
th
e
cr
o
s
s
-
v
alid
atio
n
tech
n
iq
u
es
with
5
-
f
o
ld
c
r
o
s
s
-
v
alid
atio
n
4.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
s
h
o
ws
h
o
w
well
d
if
f
er
en
t
m
ac
h
in
e
lear
n
in
g
m
o
d
els
p
r
ed
ict
th
e
tim
e
to
f
in
i
s
h
f
o
r
h
ig
h
ac
h
iev
er
s
in
v
id
e
o
-
b
ased
lea
r
n
in
g
e
n
v
ir
o
n
m
en
ts
.
Mo
d
els
g
en
er
ate
in
s
tr
u
ctio
n
s
in
J
S
ON
f
o
r
m
at
an
d
a
r
e
au
to
m
atica
lly
tr
an
s
f
er
r
e
d
f
r
o
m
th
e
L
MS
to
th
e
L
R
S
u
s
in
g
d
ata
f
r
o
m
th
e
Hass
an
I
I
Un
i
v
er
s
ity
o
f
C
asab
lan
ca
an
d
C
MI
-
5
s
p
ec
if
icatio
n
.
Desp
ite
th
eir
alm
o
s
t
p
er
f
ec
t
ac
cu
r
ac
y
(
R²
o
f
1
0
0
p
er
ce
n
t)
,
m
o
d
e
ls
s
u
ch
as
DT
,
R
F
,
an
d
g
r
a
d
ien
t
g
r
ad
in
g
r
eg
r
ess
io
n
(
GGR)
ar
e
p
r
o
n
e
t
o
o
v
e
r
f
itti
n
g
.
ANNs
p
er
f
o
r
m
e
d
eq
u
a
lly
well
(
R²
9
9
.
8
)
,
alth
o
u
g
h
with
c
o
m
p
ar
a
b
le
p
r
o
b
lem
s
o
f
o
v
er
f
itti
n
g
.
L
R
was
t
h
e
m
o
s
t
r
eliab
le
ch
o
ice,
with
a
lo
wer
p
r
o
b
ab
ilit
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
E
n
h
a
n
cin
g
lea
r
n
i
n
g
o
u
tco
mes in
s
ma
r
t e
d
u
ca
tio
n
:
A
s
u
p
ervis
ed
ma
ch
in
e
…
(
A
b
d
ella
h
B
a
k
h
o
u
yi)
4719
o
f
o
v
er
f
itti
n
g
an
d
s
tr
o
n
g
p
er
f
o
r
m
an
ce
(
R²
9
9
.
3
)
.
SVM
p
e
r
f
o
r
m
ed
wo
r
s
e,
with
an
R²
o
f
8
9
.
Nev
er
t
h
eless
,
th
e
o
v
er
all
r
esu
lts
o
f
th
e
e
n
s
em
b
le
m
eth
o
d
s
ar
e
h
ig
h
.
ACK
NO
WL
E
DG
E
M
E
NT
S
W
e
th
an
k
th
e
I
n
f
o
r
m
atio
n
Sy
s
tem
Dir
ec
to
r
ate
o
f
th
e
Hass
an
I
I
Un
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sig
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field
s:
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telli
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tern
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c
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d
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.
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