What are Bias and Variance in Machine Learning?

Machine
learning
deserves
more
scrutiny
than
ever
due
to
the
growing
adoption
of
ML
applications.
The
development
and
assessment
of
ML
models
have
become
more
complex
with
the
use
of
larger
datasets,
new
learning
requirements,
innovative
algorithms,
and
diverse
implementation
approaches. 

Therefore,
it
is
important
to
pay
attention
to
bias
and
variance
in
machine
learning
to
ensure
that

machine
learning
models
don’t
make
any
false
assumptions
or
get
filled
up
with
noise.
Machine
learning
models
must
have
the
perfect
balance
between
bias
and
variance
to
generate
results
with
better
accuracy.

In
the
development
phase,
all
the
algorithms
would
have
some
form
of
variance
and
bias.
You
can
correct
ML
models
for
bias
or
variance,
albeit
without
the
possibility
of
reducing
them
to
zero.
Let
us
learn
more
about
bias
&
variance
alongside
their
implications
for
new
machine-learning
models.

Why
Should
You
Learn
about
Bias
and
Variance?

Before
learning
about
bias
and
variance,
it
is
important
to
figure
out
why
you
should
learn
the
two
concepts.
ML
algorithms
rely
on
statistical
or
mathematical
models
that
may
feature
two
types
of
inherent
errors,
such
as
reducible
errors
and
irreducible
errors.
Irreducible
errors
are
naturally
evident
in
an
ML
model,
while
reducible
errors
can
be
controlled
and
reduced
to
improve
accuracy.

The
elements
of
bias
and
variance
in
ML
are
perfect
examples
of
reducible
errors
that
you
can
control.
Reduction
of
errors
would
demand
selection
of
models
with
the
desired
flexibility
and
complexity
alongside
access
to
relevant
training
data.
Therefore,
data
scientists
and
ML
researchers
must
have
an
in-depth
understanding
of
how
bias
is
different
from
variance.

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Flashcards

Fundamental
Explanation
of
Bias

Bias
refers
to
the
systematic
error
that
emerges
from
wrong
assumptions
made
by
the
ML
model
in
the
training
process.
You
can
also
explain
bias
in
machine
learning
in
mathematical
terms
as
the
error
emerging
from
squared
bias.
It
represents
the
extent
to
which
the
prediction
of
an
ML
model
is
different
when
compared
to
the
target
value
for
specific
training
data.
The
origins
of
bias
error
revolve
around
simplification
of
assumptions
within
ML
models
for
easier
approximation
of
the
end
results.

Model
selection
is
one
of
the
reasons
for
introducing
bias
in
ML
models.
Data
scientists
may
also
implement
resampling
to
repeat
the
model
development
process
and
derive
the
average
prediction
outputs.
Resampling
of
data
focuses
on
extraction
of
new
samples
by
leveraging
datasets
to
achieve
better
accuracy
in
results.
Some
of
the
recommended
methods
for
data
resampling
include
bootstrapping
and
k-fold
resampling. 

The
overview
of

bias
and
variance
in
machine
learning
also
points
to
the
ways
in
which
resampling
could
influence
bias.
ML
models
are
likely
to
have
a
higher
level
of
bias
when
average
final
results
are
not
the
same
as
the
actual
value
in
training
data.
All
algorithms
have
some
type
of
bias
as
they
emerge
from
assumptions
made
by
the
model
to
learn
the
target
function
easily.
Higher
bias
can
result
in
underfitting
as
the
model
cannot
capture
the
relationship
between
model
features
and
outputs.
High-bias
models
have
more
generalized
perceptions
about
the
end
results
or
target
functions.

Linear
algorithms
have
a
higher
bias,
thereby
ensuring
a
faster
learning
process.
Bias
is
the
result
of
approximation
of
complicated
real-life
problems
with
a
significantly
simpler
model
in
linear
regression
analysis.
Even
if
linear
algorithms
can
feature
bias,
it
leads
to
easily
comprehensible
outputs.
Simpler
algorithms
are
more
likely
to
introduce
more
bias
than
non-linear
algorithms. 

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Fundamental
Explanation
of
Variance 

Variance
refers
to
the
changes
in
the
target
functions
or
end
result
due
to
the
use
of
disparate
training
data.
The
explanation
for
variance
in
machine
learning
also
focuses
on
how
it
represents
the
variation
of
random
variables
from
the
expected
value.
You
can
measure
variance
by
using
a
specific
training
set.
It
serves
as
a
clear
overview
of
the
inconsistency
in
different
predictions
when
you
use
diverse
training
sets.
However,
variance
is
not
a
trusted
indicator
of
the
overall
accuracy
of
an
ML
algorithm.

Variance
is
generally
responsible
for
overfitting,
which
leads
to
magnification
of
small
variations
in
the
dataset
used
for
training.
Models
with
higher
variance
could
also
have
training
datasets
that
showcase
random
noise
rather
than
target
functions.
On
top
of
it,
the
models
can
also
determine
the
connections
between
output
variables
and
input
data.

Models
with
lower
variance
suggest
that
the
sample
data
is
closer
to
the
desired
state
of
the
model.
On
the
other
hand,
high-variance
models
are
likely
to
showcase
massive
changes
in
the
predictions
for
the
target
functions.
Examples
of
high-variance
models
include
k-nearest
neighbors,
decision
trees,
and
SVMs
or
support
vector
machines.
On
the
other
hand,
linear
regression,
linear
discriminant
analysis,
and
logistic
regression
models
are
examples
of
low-variance
ML
algorithms. 

How
Can
You
Reduce
Bias
in
ML
Algorithms?

The
ideal
way
to
fight
against
bias
and
variance
in
ML
algorithms
can
help
you
create
ML
models
with
better
performance.
You
can
find
different
methods
to
address
the
problem
of
bias
in
ML
models
to
improve
accuracy.
First
of
all,
you
can
go
for
a
more
complex
model.
Oversimplification
of
the
model
is
one
of
the
common
reasons
for
higher
bias,
as
it
could
not
capture
the
complexities
in
training
data.

Therefore,
you
have
to
make
the
ML
model
more
complex
by
reducing
the
number
of
hidden
layers
for
deep
neural
networks.
On
the
other
hand,
you
can
choose
more
complex
models,
such
as
recurrent
neural
networks
for
sequence
learning
and
convolutional
neural
networks
for
image
processing.
Complex
models
such
as
polynomial
regression
models
can
serve
as
the
ideal
fit
for
non-linear
datasets.

You
can
deal
with
bias
in
ML
algorithms
by
increasing
the
number
of
features
that
would
improve
the
complexity
of
ML
models.
As
a
result,
it
would
have
better
abilities
for
capturing
the
underlying
patterns
you
can
find
in
the
data.
Furthermore,
expanding
the
size
of
the
training
data
for
ML
models
can
help
in
reducing
bias
as
the
model
would
have
more
examples
for
learning
from
the
training
datasets. 

Regularization
of
the
model
through
techniques
like
L1
or
L2
regularization
can
help
in
preventing
overfitting
alongside
improving
generalization
features
of
the
model.
If
you
reduce
the
strength
of
regularization
or
remove
it
in
a
model
with
higher
bias,
then
you
can
enhance
its
performance
by
huge
margins. 

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How
Can
You
Reduce
Variance
in
ML
Algorithms?

ML
researchers
and
developers
must
also
know
the
best
practices
to
reduce
variance
in
ML
algorithms
to
achieve
better
performance.
You
can
find
a
clear
difference
between
bias
and
variance
in
machine
learning
by
identifying
the
measures
followed
for
reducing
variance.
The
most
common
remedial
measure
for
variance
in
ML
algorithms
is
cross-validation.

It
involves
splitting
the
data
into
training
and
testing
datasets
many
times
for
identification
of

overfitting
or
underfitting
in
a
model.
In
addition,
cross-validation
can
help
in
tuning
hyperparameters
for
reduction
of
variance.
Selection
of
the
only
relevant
features
can
help
in
reducing
complexity
of
the
model,
thereby
reducing
variance
error. 

Reduction
of
model
complexity
through
reduction
of
the
number
of
layers
or
parameters
in
neural
networks
can
help
reduce
variance
and
improve
generalization
performance.
You
can
reduce
variance
in
machine
learning
with
the
help
of
L1
or
L2
regularization
techniques.
Researchers
and
developers
can
also
rely
on
ensemble
methods
such
as
stacking,
bagging,
and
boosting
to
enhance
generalization
performance
and
reduce
variance.

Another
trusted
technique
for
reducing
variance
in
ML
algorithms
is
early
stopping,
which
helps
in
preventing
overfitting.
It
involves
stopping
the

deep
learning
model
training
when
you
don’t
find
any
improvement
in
performance
on
the
validation
set. 

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What
is
the
Bias-Variance
Tradeoff?

The
discussions
about
bias
and
variance
in
machine
learning
also
invite
attention
to
bias-variance
tradeoff.
It
is
important
to
remember
that
bias
and
variance
have
an
inverse
relationship,
thereby
suggesting
that
you
cannot
have
ML
models
with
low
bias
and
variance
or
high
bias
and
variance.
Data
engineers
working
on
ML
algorithms
to
ensure
alignment
with
a
specific
dataset
can
lead
to
lower
bias,
albeit
with
higher
variance.
As
a
result,
the
model
would
align
with
the
dataset
alongside
improving
possibilities
of
inaccuracy
in
predictions.

The
same
situation
is
applicable
in
scenarios
where
you
create
a
low
variance
model
that
showcases
higher
bias.
It
may
reduce
the
risk
of
inaccuracy
in
predictions,
albeit
with
a
lack
of
alignment
between
the
model
and
the
dataset.
The
bias-variance
tradeoff
refers
to
the
balance
between
bias
and
variance.
You
can
address
the
bias-variance
tradeoff
by
increasing
the
training
dataset
and
the
complexity
of
the
model.
It
is
also
important
to
remember
that
the
type
of
model
plays
a
major
role
in
determining
the
tradeoff. 

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Final
Words 

The
review
of
the
difference
between
bias
and
variance
in
machine
learning
shows
that
it
is
important
to
address
these
two
factors
before
creating
any
ML
algorithm.
Variance
and
bias
errors
are
major
influences
on
the
possibilities
for
overfitting
and
underfitting
in

machine
learning.
Therefore,
the
accuracy
of
ML
models
depends
significantly
on
bias
and
variance.
At
the
same
time,
it
is
also
important
to
ensure
the
right
balance
between
variance
and
bias.
It
can
help
you
achieve
better
results
from

machine
learning
algorithms.
Discover
more
insights
on
bias
and
variance
to
understand
their
importance
now.