a supervised learning algorithm takes in
both a known set of input data and
corresponding output data it then trains
a model to map inputs to outputs so it
can predict the response to any new set
of input data as we've previously
discussed all supervised learning
techniques take the form of either
classification or regression
classification techniques predict
discrete responses use these techniques
if the outputs you want to predict can
be separated into different groups
examples of classification problems
include medical imaging speech
recognition and credit scoring
regression techniques on the other hand
predict continuous responses a good
example of this is any application where
the output you are predicting can take
any value in a certain range like stock
prices and acoustic signal processing
now let's say you have a classification
problem you're trying to solve let's
take a brief look at just a few
classification algorithms you could use
the logistic regression algorithm is one
of the simplest it is used with binary
classification problems meaning problems
where there are only two possible
outputs it works best when the data can
be well separated by a single linear
boundary you can also use it as a
baseline for comparison against more
complex classification methods bagged
and boosted decision trees combine
individual decision trees which have
less predictive power into an ensemble
of many trees which has greater
predictive power it is best used when
predictors are discrete or behave none
linearly and when you have more time to
train a model keep in mind there are
many other classification algorithms
these are just two of the most common
there are plenty of algorithms to choose
from if you have a regression problem as
well linear regression is a statistical
modeling technique use it when you need
an algorithm that is easy to interpret
and fast a fit or as a baseline for
evaluating other more complex regression
models non linear regression helps
describe more complex relationships and
data use it when data has strong
nonlinear trends
and cannot be easily transformed into a
linear space again these are just two
common regression algorithms you can
choose from there are many more you
might want to consider now let's put it
all together and see how this process
might look in the real world say you're
an engineer at a plastic production
plant the plants 900 workers operate 24
hours a day 365 days a year to make sure
you catch machine failures before they
happen you need to develop a health
monitoring and predictive maintenance
application that uses advanced machine
learning algorithms to classify
potential issues after collecting
cleaning and logging data from the
machines in the plant your team
evaluates several classification
techniques for each technique the team
trains a classification model using the
machine data and then tests the models
ability to predict if a machine is about
to have a problem the tests show that an
ensemble of bad decision trees is the
most accurate so that's what your team
moves forward with when developing the
predictive maintenance application in
addition to trying different types of
models there are many ways to further
increase your models predictive power
let's briefly talk about just three of
these methods the first is feature
selection where you identify the most
relevant inputs from the data that
provide the best predictive power
remember a model can only be as good as
the features you use to train it second
feature transformation is a form of
dimensionality reduction which we
discussed in the previous video here are
the three most commonly used techniques
with feature transformation you reduce
the complexity of your data which can
make it much easier to represent and
analyze hyper parameter tuning is a
third way to increase your models
accuracy it is an iterative process
where your goal is to find the best
possible settings for how to train the
model you retrain your model many times
using different settings until you
discover the combination of settings
that results in the most accurate model
so that's a quick look at supervised
learning in our next video we're going
to take a deeper look at an example
machine learning workflow until then be
sure to check out the description
below four more machine learning
resources thanks for watching