What is Underfitting and Overfitting in Machine Learning?

Machine
learning
focuses
on
developing
predictive
models
that
can
forecast
the
output
for
specific
input
data.
ML
engineers
and
developers
use
different
steps
to
optimize
the
trained
model.
On
top
of
it,
they
also
determine
the
performance
of
different

machine
learning
models
by
leveraging
different
parameters. 

However,
choosing
a
model
with
the
best
performance
does
not
mean
that
you
have
to
choose
a
model
with
the
highest
accuracy.
You
need
to
learn
about
underfitting
and
overfitting
in
machine
learning
to
uncover
the
reasons
behind
poor
performance
of
ML
models.

Machine
learning
research
involves
the
use
of
cross-validation
and
train-test
splits
to
determine
the
performance
of
ML
models
on
new
data.
Overfitting
and
underfitting
represent
the
ability
of
a
model
to
capture
the
interplay
between
input
and
output
for
the
model.
Let
us
learn
more
about
overfitting
and
underfitting,
their
causes,
potential
solutions,
and
the
differences
between
them.

Exploring
the
Impact
of
Generalization,
Bias,
and
Variance 

The
ideal
way
to
learn
about
overfitting
and
underfitting
would
involve
a
review
of
generalization,
bias,
and
variance
in
machine
learning.
It
is
important
to
note
that
the
principles
of
overfitting
and
underfitting
in
machine
learning
are
closely
related
to
generalization
and
bias-variance
tradeoffs.
Here
is
an
overview
of
the
crucial
elements
that
are
responsible
for
overfitting
and
underfitting
in
ML
models.

• 
  Generalization 

Generalization
refers
to
the
effectiveness
of
an
ML
model
in
applying
the
concepts
they
learned
to
specific
examples
that
were
not
a
part
of
the
training
data.
However,
generalization
is
a
tricky
issue
in
the
real
world.
ML
models
use
three
different
types
of
datasets:
training,
validation,
and
testing
sets.
Generalization
error
points
out
the
performance
of
an
ML
model
on
new
cases,
which
is
the
sum
of
bias
error
and
variance
error.
You
must
also
account
for
irreducible
errors
that
come
from
noise
in
the
data,
which
is
an
important
factor
for
generalization
errors. 

Bias
is
the
result
of
errors
due
to
extremely
simple
assumptions
made
by
ML
algorithms.
In
mathematical
terms,
bias
in
ML
models
is
the
average
squared
difference
between
model
predictions
and
actual
data.
You
can
understand
underfitting
in
machine
learning
by
finding
out
models
with
higher
bias
errors.
Some
of
the
notable
traits
of
models
with
higher
bias
include
higher
error
rates,
more
generalization,
and
failure
to
capture
relevant
data
trends.
High-bias
models
are
the
most
likely
candidates
for
underfitting.

• 
  Variance   

Variance
is
another
prominent
generalization
error
that
emerges
from
the
excessive
sensitivity
of
ML
models
to
subtle
variations
in
training
data.
It
represents
the
change
in
the
performance
of
ML
models
during
evaluation
with
respect
to
validation
data.
Variance
is
a
crucial
determinant
of
overfitting
in
machine
learning,
as
high-variance
models
are
more
likely
to
be
complex.
For
example,
models
with
multiple
degrees
of
freedom
showcase
higher
variance.
On
top
of
that,
high-variance
models
have
more
noise
in
the
dataset,
and
they
strive
to
ensure
that
all
data
points
are
close
to
each
other.

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Definition
of
Underfitting
in
ML
Models 

Underfitting
refers
to
the
scenario
in
which
ML
models
cannot
accurately
capture
the
relationship
between
input
and
output
variables.
Therefore,
it
can
lead
to
a
higher
error
rate
on
the
training
dataset
as
well
as
new
data.
Underfitting
happens
due
to
over-simplification
of
a
model
that
can
happen
due
to
a
lack
of
regularization,
more
input
features,
and
more
training
time.
Underfitting
in
ML
models
leads
to
training
errors
and
loss
of
performance
due
to
the
inability
to
capture
dominant
trends
in
the
data. 

The
problem
with
underfitting
in
machine
learning
is
that
it
does
not
allow
the
model
to
generalize
effectively
for
new
data.
Therefore,
the
model
is
not
suitable
for
prediction
or
classification
tasks.
On
top
of
that,
you
are
more
likely
to
find
underfitting
in
ML
models
with
higher
bias
and
lower
variance.
Interestingly,
you
can
identify
such
behavior
when
you
use
the
training
dataset,
thereby
enabling
easier
identification
of
underfitted
models.

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Definition
of
Overfitting
in
ML
Models

Overfitting
happens
in
machine
learning
when
an
algorithm
has
been
trained
closely
or
exactly
according
to
its
training
dataset.
It
creates
problems
for
a
model
in
making
accurate
conclusions
or
predictions
for
any
new
data.
Machine
learning
models
use
a
sample
dataset
for
training,
and
it
has
some
implications
for
overfitting.
If
the
model
is
extremely
complex
and
trains
for
an
extended
period
on
the
sample
data,
then
it
could
learn
the
irrelevant
information
in
the
dataset. 

The
consequence
of
overfitting
in
machine
learning
revolves
around
the
model
memorizing
the
noise
and
fitting
closely
with
the
training
data.
As
a
result,
it
would
end
up
showcasing
errors
for
classification
or
prediction
tasks.
You
can
identify
overfitting
in
ML
models
by
checking
higher
variance
and
low
error
rates.

How
Can
You
Detect
Underfitting
and
Overfitting?

ML
researchers,
engineers,
and
developers
can
address
the
problems
of
underfitting
and
overfitting
with
proactive
detection.
You
can
take
a
look
at
the
underlying
causes
for
better
identification.
For
example,
one
of
the
most
common
causes
of
overfitting
is
the
misinterpretation
of
training
data.
Therefore,
the
model
would
lead
to
limited
accuracy
in
results
for
new
data
even
if
overfitting
leads
to
higher
accuracy
scores. 

The
meaning
of
underfitting
and
overfitting
in
machine
learning
also
suggests
that
underfitted
models
cannot
capture
the
relationship
between
input
and
output
data
due
to
over-simplification.
As
a
result,
underfitting
leads
to
poor
performance
even
with
training
datasets.
Deploying
overfitted
and
underfitted
models
can
lead
to
losses
for
businesses
and
unreliable
decisions.
Take
a
look
at
the
proven
ways
to
detect
overfitting
and
underfitting
in
ML
models.

• 
  Finding
  Overfitted
  Models 

You
can
explore
opportunities
to
detect
overfitting
across
different
stages
in
the
machine
learning
lifecycle.
Plotting
the
training
error
and
validation
error
can
help
identify
when
overfitting
takes
shape
in
an
ML
model.
Some
of
the
most
effective
techniques
to
detect
overfitting
include
resampling
techniques,
such
as
k-fold-cross-validation.
You
can
also
hold
back
a
validation
set
or
choose
other
methods,
such
as
using
a
simplistic
model
as
a
benchmark.

• 
  Finding
  Underfitted
  Models

The
basic
understanding
of
overfitting
and
underfitting
in
machine
learning
can
help
you
detect
the
anomalies
at
the
right
time.
You
can
find
problems
of
underfitting
by
using
two
different
methods.
First
of
all,
you
must
remember
that
the
loss
for
training
and
validation
will
be
significantly
higher
for
underfitted
models.
Another
method
to
detect
underfitting
involves
plotting
a
graph
with
data
points
and
a
fixed
curve.
If
the
classifier
curve
is
extremely
simple,
then
you
might
have
to
worry
about
underfitting
in
the
model.

How
Can
You
Prevent
Overfitting
and
Underfitting
in
ML
Models?

Underfitting
and
overfitting
have
a
significant
influence
on
the
performance
of
machine
learning
models.
Therefore,
it
is
important
to
know
the
best
ways
to
deal
with
the
problems
before
they
cause
any
damage.
Here
are
the
trusted
approaches
for
resolving
underfitting
and
overfitting
in
ML
models.

• 
  Fighting
  against
  Overfitting
  in
  ML
  Algorithms

You
can
find
different
ways
to
deal
with
overfitting
in
machine
learning
algorithms,
such
as
adding
more
data
or
using
data
augmentation
techniques.
Removal
of
irrelevant
aspects
from
the
data
can
help
in
improving
the
model.
On
the
other
hand,
you
can
also
go
for
other
techniques,
such
as
regularization
and
ensembling.

• 
  Fighting
  against
  Underfitting
  in
  ML
  Algorithms

The
best
practices
to
address
the
problem
of
underfitting
include
allocating
more
time
for
training
and
eliminating
noise
from
data.
In
addition,
you
can
deal
with
underfitting
in
machine
learning
by
choosing
a
more
complex
model
or
trying
a
different
model.
Adjustment
of
regularization
parameters
also
helps
in
dealing
with
overfitting
and
underfitting. 

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Exploring
the
Difference
between
Overfitting
and
Underfitting 

The
fundamental
concepts
provide
relevant
answers
to
the
question,
“What
is
the
difference
between
overfitting
and
underfitting
machine
learning?”
on
different
parameters.
For
example,
you
can
notice
the
differences
in
the
methods
used
for
detecting
and
curing
underfitting
and
overfitting.
Underfitting
and
overfitting
are
the
prominent
reasons
behind
lack
of
performance
in
ML
models.
You
can
understand
the
difference
between
them
with
the
following
example.

Let
us
assume
that
a
school
has
appointed
two
substitute
teachers
to
take
classes
in
absence
of
regular
teachers.
One
of
the
teachers,
John,
is
an
expert
at
mathematics,
while
the
other
teacher,
Rick,
has
a
good
memory.
Both
the
teachers
were
called
up
as
substitutes
when
the
science
teacher
did
not
turn
up
one
day. 

John,
being
an
expert
at
mathematics,
failed
to
answer
some
of
the
questions
that
students
asked.
On
the
other
hand,
Rick
had
memorized
the
lesson
that
he
had
to
teach
and
could
answer
questions
from
the
lesson.
However,
Rick
failed
to
answer
questions
that
were
about
complexly
new
topics. 

In
this
example,
you
can
notice
that
John
has
learned
from
a
small
part
of
the
training
data,
i.e.,
mathematics
only,
thereby
suggesting
underfitting.
On
the
other
hand,
Rick
can
perform
well
on
the
known
instances
and
fails
on
new
data,
thereby
suggesting
overfitting. 

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ways
to
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Final
Words 

The
explanation
for
underfitting
and
overfitting
in

machine
learning
showcases
how
they
can
affect
the
performance
and
accuracy
of

ML
algorithms.
You
are
likely
to
encounter
such
problems
due
to
the
data
used
for
training
ML
models.
For
example,
underfitting
is
the
result
of
training
ML
models
on
specific
niche
datasets.

On
the
other
hand,
overfitting
happens
when
the
ML
models
use
the
whole
training
dataset
for
learning
and
end
up
failing
for
new
tasks.
Learn
more
about
underfitting
and
overfitting
with
the
help
of
professional
training
courses
and
dive
deeper
into
the
domain
of
machine
learning
right
away.