👂🎴 🕸️
Supervised
learning
parallels
human
learning
through
its
reliance
on
guidance
from
labeled
examples
''
similar
to
how
humans
learn
with
feedback
.
For
instance
''
when
a
child
learns
to
identify
objects
''
they
receive
input
(
the
object
)
and
a
corresponding
label
(
e
.
g
.''
„
dog
“
or
„
apple
“
)
from
a
teacher
or
parent
.
Mistakes
are
corrected
''
reinforcing
the
connection
between
input
and
label
''
much
like
how
supervised
learning
algorithms
adjust
their
predictions
based
on
errors
.
Categorization
is
a
fundamental
human
cognitive
process
where
we
group
objects
''
ideas
''
or
experiences
based
on
shared
characteristics
.
Features
are
observable
and
measurable
properties
or
characteristics
used
to
describe
data
in
both
machine
learning
and
human
experience
. <
div
><
br
>
div
><
div
>
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
. <
div
><
br
>
div
><
div
>
In
human
experience
''
features
represent
sensory
or
cognitive
details
like
color
''
texture
''
pitch
''
or
emotional
tone
''
helping
interpret
and
navigate
the
world
.
div
>
div
>
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
.<
p
class
=
„
fragment
“
>
Visual
modality
:
Color
''
shape
''
size
''
motion
''
texture
(
raw
);
patterns
''
symmetry
''
depth
cues
(
derived
)
p
><
p
class
=
„
fragment
“
>
Auditory
modality
:
pitch
''
volume
''
tempo
''
rhythm
(
raw
);
speech
patterns
''
tone
of
voice
(
derived
)
p
><
p
class
=
„
fragment
“
>
haptics
?
emotions
?
language
?
p
>
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
:<
p
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
).
p
><
p
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
. <
div
><
br
>
div
><
div
>
In
images
''
this
includes
methods
like
SIFT
''
SURF
''
and
Haar
cascades
for
detecting
edges
''
corners
''
or
keypoints
. 
div
><
div
><
br
>
div
><
div
>
In
audio
''
algorithms
like
MFCCs
extract
time
-
frequency
characteristics
''
while
text
relies
on
tokenization
and
n
-
grams
. <
br
>
div
>
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
.<
div
> 
div
><
div
>
Feature
selection
process
is
crucial
in
high
-
dimensional
datasets
''
enabling
models
to
concentrate
on
the
most
impactful
data
while
discarding
irrelevant
or
redundant
features
.
div
>
A
classifier
in
machine
learning
is
a
model
or
algorithm
designed
to
categorize
data
into
predefined
groups
or
labels
.
It
takes
input
data
''
analyzes
its
features
''
and
assigns
it
to
a
specific
class
based
on
learned
patterns
from
training
data
.
For
example
''
a
classifier
might
identify
whether
an
email
is
spam
or
not
spam
''
or
recognize
handwritten
digits
.
Classifiers
are
essential
in
supervised
learning
tasks
and
operate
by
minimizing
errors
in
predictions
through
training
on
labeled
datasets
.
Common
types
include
neural
networks
''
support
vector
machines
''
decision
trees
etc
.
A
Decision
Tree
is
a
visual
and
intuitive
machine
learning
method
that
makes
decisions
by
asking
questions
about
the
data
.
It
works
like
a
flowchart
''
starting
with
a
question
at
the
top
and
branching
out
based
on
answers
.
These
questions
are
not
just
yes
/
no
''
but
can
also
be
comparisons
''
like
„
Is
the
age
greater
than
18
?
“
or
„
Is
the
temperature
below
30
°
C
?
“
Each
split
is
chosen
using
a
quantitative
measure
''
such
as
information
gain
or
Gini
impurity
''
to
find
the
best
threshold
for
separating
the
data
.
A
Support
Vector
Machine
(
SVM
)
is
a
machine
learning
method
that
helps
divide
data
into
categories
.
Imagine
drawing
a
line
(
or
boundary
)
on
a
graph
to
separate
different
groups
of
points
''
like
cats
and
dogs
.
SVM
finds
the
best
line
that
keeps
the
groups
as
far
apart
as
possible
.
For
trickier
data
''
it
can
use
special
math
(
called
kernels
)
to
draw
curves
or
work
in
higher
dimensions
.
In
supervised
machine
learning
''
training
is
the
process
of
teaching
a
model
''
like
a
classifier
''
to
make
accurate
predictions
by
learning
patterns
from
labeled
data
.
Each
data
point
in
the
training
set
includes
features
(
characteristics
or
inputs
that
describe
the
data
''
like
size
or
color
)
and
a
corresponding
label
(
the
correct
output
or
category
).
The
model
uses
this
data
to
adjust
its
internal
parameters
''
minimizing
the
error
between
its
predictions
and
the
actual
labels
.
This
is
done
through
algorithms
like
gradient
descent
.
The
goal
is
to
generalize
from
the
training
data
''
enabling
the
classifier
to
make
accurate
predictions
on
new
''
unseen
data
.
In
supervised
machine
learning
''
*
testing
*
(
or
*
inference
*)
is
the
process
of
evaluating
a
trained
model
'
s
ability
to
make
accurate
predictions
on
new
''
unseen
data
.
During
this
phase
''
the
model
is
given
data
points
with
*
features
*
(
inputs
like
size
or
color
)
but
without
the
labels
it
was
trained
on
.
The
model
uses
the
patterns
it
learned
during
training
to
predict
the
labels
for
this
data
.
The
results
are
then
compared
to
the
actual
labels
(
if
available
)
to
measure
the
model
'
s
performance
using
metrics
like
accuracy
or
precision
.
Inference
is
the
final
application
of
the
model
to
make
real
-
world
predictions
.
Binary
classifiers
are
evaluated
by
comparing
their
predictions
to
the
actual
outcomes
using
a
confusion
matrix
.
This
is
a
table
with
four
categories
:
True
Positives
(
TP
)''
where
the
classifier
correctly
predicts
a
positive
outcome
;
True
Negatives
(
TN
)''
where
it
correctly
predicts
a
negative
outcome
;
False
Positives
(
FP
)''
where
it
wrongly
predicts
a
positive
;
and
False
Negatives
(
FN
)''
where
it
misses
a
positive
case
.
Metrics
like
accuracy
(
overall
correctness
)''
precision
(
focus
on
positives
)''
and
recall
(
how
well
positives
are
found
)
are
calculated
from
this
matrix
''
helping
to
assess
the
classifier
’
s
performance
.
In
supervised
machine
learning
''
*
validating
*
is
the
process
of
fine
-
tuning
and
assessing
a
model
'
s
performance
during
training
to
ensure
it
generalizes
well
to
unseen
data
.
Unlike
testing
''
validation
occurs
on
a
separate
*
validation
set
*''
distinct
from
both
training
and
testing
data
.
The
model
uses
the
*
features
*
of
this
set
to
make
predictions
''
which
are
compared
to
the
actual
labels
to
calculate
metrics
like
accuracy
or
loss
.
This
helps
monitor
overfitting
or
underfitting
and
guides
adjustments
to
model
parameters
or
hyperparameters
(
e
.
g
.''
learning
rate
or
regularization
).
Validation
ensures
the
classifier
is
optimized
before
its
final
evaluation
on
the
test
set
.
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