👂 🎴 🕸️
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
.
<
div
>
You
will
form
teams
of
two
and
use
the
training
data
to
develop
criteria
for
distinguishing
biting
from
non
-
biting
monkeys
.
These
must
be
clearly
noted
so
that
they
can
be
applied
to
new
examples
by
another
team
afterwards
.
A
possibility
to
record
the
criteria
is
a
decision
tree
.
It
should
be
the
goal
that
the
existence
or
absence
of
a
particular
feature
permits
a
clear
assignment
to
one
of
the
groups
.
The
use
of
decision
trees
is
optional
''
alternatively
''
it
is
also
possible
to
 
explicitly
write
down
decision
rules
.
<
br
/><
br
/>
At
the
end
ofthe
training
phase
''
the
criteria
formulated
are
exchanged
with
another
team
.
Now
''
the
students
are
shown
the
pictures
of
the
remaining
monkeys
(
test
data
)
one
after
the
other
.
For
each
image
''
the
teams
decide
whether
the
monkey
will
bite
or
not
using
the
scheme
of
rules
developed
by
their
classmates
...
div
>
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
.
[Impressum, Datenschutz, Login] Other subprojects of wizzion.com linkring: fibel.digital puerto.life naadam.info gardens.digital udk.ai refused.science teacher.solar baumhaus.digital kyberia.de giver.eu