👂🎴 🕸️
Features
are
observable
and
measurable
properties
or
characteristics
used
to
describe
data
in
both
machine
learning
and
human
experience
. <
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>
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>
In
ML
''
features
are
input
variables
—
raw
(
e
.
g
.''
pixel
intensities
''
audio
waveforms
)
or
engineered
(
e
.
g
.''
embeddings
''
statistical
summaries
)—
that
models
use
to
make
predictions
. <
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>
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>
In
human
experience
''
features
represent
sensory
or
cognitive
details
like
color
''
texture
''
pitch
''
or
emotional
tone
''
helping
interpret
and
navigate
the
world
.
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In
human
experience
''
features
can
be
thought
of
as
the
observable
or
measurable
characteristics
that
we
use
to
interpret
and
make
decisions
about
the
world
around
us
.
These
features
can
come
from
different
sensory
modalities
or
cognitive
processes
.<
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„
features
“
>
Visual
modality
:
Color
''
shape
''
size
''
motion
''
texture
(
raw
);
patterns
''
symmetry
''
depth
cues
(
derived
)
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><
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class
=
„
features
“
>
Auditory
modality
:
pitch
''
volume
''
tempo
''
rhythm
(
raw
);
speech
patterns
''
tone
of
voice
(
derived
)
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><
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class
=
„
features
“
>
haptics
?
emotions
?
language
?
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>
Features
represent
the
input
variables
used
by
a
machine
learning
model
to
make
predictions
or
classifications
.
They
are
the
building
blocks
of
the
dataset
and
provide
the
information
necessary
for
the
model
to
learn
relationships
and
patterns
.
Features
can
be
:<
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class
=
„
fragment
“
>
Numerical
:
Continuous
or
discrete
values
(
e
.
g
.''
height
''
number
of
words
).
p
><
p
class
=
„
fragment
“
>
Categorical
:
Representing
distinct
groups
(
e
.
g
.''
color
''
category
labels
).
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><
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class
=
„
fragment
“
>
Derived
:
Transformed
or
engineered
values
combining
raw
data
(
e
.
g
.''
ratios
''
log
values
).
p
>
Feature
detection
is
the
process
of
identifying
significant
patterns
''
structures
''
or
attributes
in
raw
data
to
aid
analysis
and
decision
-
making
. <
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><
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>
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>
In
images
''
this
includes
methods
like
SIFT
''
SURF
''
and
Haar
cascades
for
detecting
edges
''
corners
''
or
keypoints
. 
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>
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>
In
audio
''
algorithms
like
MFCCs
extract
time
-
frequency
characteristics
''
while
text
relies
on
tokenization
and
n
-
grams
. <
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>
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>
Feature
selection
involves
choosing
the
most
relevant
features
from
a
dataset
to
improve
model
accuracy
''
reduce
overfitting
''
and
enhance
computational
efficiency
.
Techniques
include
filters
(
e
.
g
.''
chi
-
square
tests
)''
wrappers
(
e
.
g
.''
recursive
feature
elimination
)''
and
embedded
methods
like
LASSO
.
Boosting
algorithms
(
e
.
g
.''
AdaBoost
''
Gradient
Boosting
''
XGBoost
)
also
inherently
perform
feature
selection
by
iteratively
focusing
on
features
with
the
highest
predictive
power
.<
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> 
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><
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>
Feature
selection
process
is
crucial
in
high
-
dimensional
datasets
''
enabling
models
to
concentrate
on
the
most
impactful
data
while
discarding
irrelevant
or
redundant
features
.
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>
Classifiers
Training
Validating
Testing
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