data Science is the most demanded domain but
they did you ever Why It's getting the popularity
Let me tell you why data that science
is getting the popularity because we have lots
of data right now with us each and
every moment we are actually making our leader
to process unstructured awesome structure data We need
a proper V or proper process on That's
what Leader science comes into the picture It
a sense has lots off tools and mission
learning and be planning algorithms which help us
to process those unstructured data on If you
also want to make your career in data
signs there are some part for you need
to know properly when you go for any
data science interview questions to keep that in
mind we came up with a tutorial the
B designs interview question But they said the
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tutorial in this tutorial will have a new
pack What is designs then We've been jumping
Do Why do we need it A science
on what is the life cycle of be
designs Then they will have a new back
some of the most used interview portion If
you are a enthusiasts or if you want
to prepare your period for data science then
I should suggest you go for full video
It will help you to understand what ago
critical question you have to face when you
go for any interview questions right So let's
get started So first we will have a
look at why we needed to signs before
Give the example Why do immediate assigned sleeping
give you are real time scenario that why
did assigns becoming so popular with things So
in the past what we used to have
used to have data future in a structured
manner on the amount of the data is
lease but the time we can see that
our data is becoming unstructured or semi structure
So and the number of data amount of
data become massive So in this case we
need some of the tools which can handle
this massive amount off on subject or Senate
Such a bitter on Both Peters actually can
be used to get a proper business inside
on the hidden pattern from them So we
know I'm talking about any hidden pattern What
I don't see them back in Right So
let me give you his time Example for
that You can understand what I go for
Hidden pattern Suppose they are really going for
on light society right So all the time
What What if we are going toe Like
maybe we are going to go for buying
a food all Maybe we're going to book
up tab right that then we use online
transactions or online booking facilities So in that
case we do everything that you are giving
a better through this company's Andrea capturing those
data But this data's amount these are huge
Um but they don't have a proper structure
form So this is how this d does
this a massive amount of data give you
a proper business insights But before getting those
insides you need to actually people system I'm
coming to the hidden patterns So basically they
go for anyone records Actually we know that
there are multiple transaction there fraud But how
can you understand that the does transactional for
transaction in this case we need to go
for analyzing our data Maybe there are some
attributes they're related to that fraud transaction So
if you go for checking those datas if
you if we go for analyzing those data
who will give this having hidden patterns who
understand that what are the attributes actually directly
connected with the throat transaction So this is
why each and every company right now are
using their data get a proper inside for
business But processing those huge number of data
using the traditional tool is not possible So
for that we go for big designs applications
Now let's have a look at what actually
did a science stands for so be doesn't
is basically a process It's not a single
fast food is a whole proper process to
get some meaningful information from the massive amount
off Peter in some symptoms read on starting
a data to get proper intuitive insights Adidas
raises a mixture of various tools algorithms Lishi
learning and be planning concepts to discover hidden
pattern from the raw on instructor Now let's
have a new bag What is the life
cycle of ardent aside So will become to
life cycle the basic part off that later
sciences you need to understand the business problem
So big data scientists they maximum usedto interact
with the stockholders who understand that business problems
Okay so fast when you understand the business
business problem that after that you can go
for it Like me basically going for objectives
right What objective unit to go for then
Comes up Data acquisition part for these data
acquisition data acquisition is basically stance for collecting
the data if you don't have data harkening
analyzing Right So one of the main basic
part off analyzing Sorry Collecting the leader So
collecting the data is one off the mean
after you collect your data We all know
those data as a structure or since structure
Peter Then we go for cleaning those feet
after we clean our data But this process
is one of the most time Comes mean
process because it takes a lot of time
to clean your those on such a doctor
Maybe you have misspells attributes if you have
some under relevant So Peter killing is one
of the most Ankle time comes human part
because maybe you will have a misspells attribute
Are some unknown data some irrelevant Oh data
Right They're not at all helping you to
do the analysing part right So in this
case data cleaning is most off Most time
comes in part Bill comes to exploit every
detail So bodies exploding determines is so It's
predicated analysis Basically where area B days Rabia
value a leader is structured that then you
can do some statistical concepts So those are
mystical concepts Helps you to understand your leader
What is the speed off your data Right
What are the outlier off your data What
You are data actually Constance off one of
the attributes there really coordinated to each other
So this is why we use exploited in
data analysis spot Now when you have a
proper idea into an idea about your data
that then you are quite sure that each
algorithm you should go for to analyze what
it right So that time we used Michelle
learning techniques So after wind up mission learning
techniques after making your model with the Decatur
using those machine learning algorithms then we go
for protecting our morning But why do we
need protection Because maybe you give up Peter
to your model which did Actually the trading
does it right But after you are trading
you see that is a nice a currency
you got from your model But when you
are giving a non data to your model
your model it's failed to capture to understand
those data So that means your model it's
not really toe go for leader production part
right So prediction is one of the most
important part of India making the model when
you see that after giving up I'm not
later to your model as well Your model
is quite like walking quite fine with that
that you can say OK your modern is
ready Now you can deploy your modern to
the cloud For that someone came Use your
moment So this is the one scenario or
one lifecycle offer data signs Now let's have
a look at what are the tools they
used for data signs So these are the
basic tools I'm talking about over here But
there are my capitals We having data signs
We have a stool to managing the data
That's behalf Imo informatica fight on job Mac
Labs says this is the basic truth When
we talk about any Peter signs languages right
now let's have a look at the interview
portion But you can actually expect when you
go for any interview Oh for a leader
Scientists Right So fast Pulse portion is how
do you have a missing value imputation for
quantitative values So this is the fight off
again Out It's brought it down This is
when you were actually handle in your construction
details So basically there are four options we
have maybe replace them with me He plays
them with more replace them with media or
none of that So your answer will be
over here that replace them with me right
now Let's have a look at this second
pushing how you compare wanted it in baby
about so your answer will be We didn't
compare them using correlation change off anyone variable
can if it the other way on it
can be done in started or dark thoughts
graphical So we you started lot or dot
blots basically to understand and to visualize your
data Now let's have a look at that
Her portion What is beyond morning on What
are the meters dependent on it So what
is DS stance for So basically Dre's decision
estimation like so decision is physical sign for
beside like yes or no risky or notice
not risky estimation is basically how much on
being the video been at Estimators I'm Ran
start for knocking them according Battered is what
is more risky Sadistically technics mission learning and
advance allergies techniques are dependent on the the
model Now let's have a look at what
is for worked immigration So far indignation is
one off the automated mechanism off model selection
It starts with the video but I'm people
adding very very freely through just special Now
you stay pontification so it is one of
the automated mechanism off more than selection that
it starts with a variable onto some point
it starts dropping variables So this is these
are the two times obligation we seen so
far Now let's have a look back What
is C A s I mean what range
it varies right So very sad for billions
Inflation pacto on VH SS with a factor
correlated to each other that is multi Call
it reality which could affect the values on
the modernism going to be as well if
the eyes is gonna maintain problem off multi
community offers right So this is how we
actually go for using via status variants inflation
factor Now let's have a look back What
is Fisher's exact feast So fishes exact taste
is a status symbol Significant stays using analysts
off contingency tables although in practice it is
employed when sample sizes are small it is
valley for all the sample sizes Now let's
have a look at what is correlation and
water It's measures So basically correlation is a
process to measure where the two tables are
related or not Status for it is some
classical we often present in the relationship majors
off correlations like strength off relationship on P
essence coordination number Let's have a look at
what are the types of simple I'm mild
depend revision This is one of the most
important question when you go for any interview
So what is the elevation so insincerely Immigration
We have two parts One is linear and
one is not only on under linear We
have continuous on linear immigration on in Armenia
we have poisoned logistic my binomial Only no
motivation and going to be gone for might
be my deep integration We have linear No
Now let's have a look at what you
mean by means quite a bit So basically
means quite it means is the average of
spray all the others That is average squared
difference between the estimated values and the actual
values Now we came to the 11 number
potion His ask where Goodness off it So
what will be our answer Ask where is
a good missile fate major for linear innovation
models So this had sticking because the present
is all very in single dependent Variable back
the independent variables explaining collectively for instance small
Ask where values are not always a problem
on the highest values are not necessarily good
So this is how we can see We
can see the r squared off aspirin is
basically goodness off feet Now let's have you
back What is the difference between I'm easy
on Ask where it status sticks So I
miss Is the square root off the variance
off Very She tickles It indicates the absolute
feet off the model to the beat How
does the absurd data points are Do the
mortars predicted Van There's a sweat is a
really major all feet I am It is
an absolute mission or feet In addition adjusted
a square more than 0.75 is a good
value for showing accuracy In some cases on
After adjusted a square off 0.4 or more
is acceptable as but no I'm somewhat of
the state off Arctic meets important Rick organization
I'm issuing the two fundamental problem in mission
learning A statistical analysis on algorithm design The
former failed US the principal off mathematical models
that we establishes from the observation detail The
later defines the conditions on which implementation off
data models and be Does it rely on
the business off the Soviet pattern Mechanization techniques
can be categorized into six parts This includes
statistical techniques structural technics template matching new relate
walk approached fuzzy model at hybrid models Now
let's go to the next portion What is
the difference between Geraldine I mean your models
Nice lenient mortars Now we will have a
look at what is generally union model So
generally via model is a station case off
General Vinje on generalize media model is an
extension off generally Nia morning This is quite
freaky right So basically generally me a model
is a special case off generally near modern
but generalized senior model is an extension off
generally nya morning in general Levy a model
response variable need to be continuous But in
the case off generalized union model response variable
can also be categorical like it's on nights
In general any in modern distribution off residue
was is expected to be normal But in
the case off generous million model distribution often
city was can be known normal That is
like binomial form Largest Mourners now even see
the link function is a simple identity function
like a 50 equals Tuesday for general even
your modern But in the case of generous
Simeon model include a link function batteries the
meat off responses to the linear combination off
predictors in the model like college function inverse
function It's a truck on the list Square
meter is used to estimate the parameters off
the model In the case of General Linear
Models on for generalized Linear Model you Square
metal resumes to estimate the time just off
the modern So both the case is the
least choir model is used basically to estimate
the parameters of the model Now let's have
a look at one of the best application
off linear irrigation so basically linear regression can
be used to optimize that business process But
before going into the answer let me tell
you bodies are immigration So liberation is basically
finding out our relationship to a relationship between
two regulars Eso let me give you an
example Let what actually want to mean by
that Suppose our sales off ice cream is
decreasing in the season off Vinto but we
have a season of summer season that time
Cesar feistiness is increasing so we can see
there is a direct relation between seasons on
the ice Princess who established those relationship between
this true variable We need to use algorithms
so that time we go for using may
be lenient regulation or some other algorithms right
So this is actually stands for probation So
now now it's having you cats was the
best application off United Nations So already we
say that the new immigration can be used
to optimize a business problem Suppose a big
car an estimated by temperature for its open
who increase the self life off it Cookies
Oh maybe in V PIOs are called Kate
feels we can analyze the relationship between the
wait times off color on the number of
complaints or maybe even take some other example
like Imagine you want to estimate the demand
off your customer or you want to predict
the cells off particular items in the future
that time Also we can go fund me
near the patient or the last example Cabbie
Insurance company companies rely heavily on immigration toe
Understand the number off claims at any given
time our populations off falling on vacation So
these are the youth case our business application
off Lydia Re picture Now let's have a
look at what are the assumption off any
revision This is one of the most asked
question for interview up So what are the
assumption be half a limited mission The regression
model is linear in perimeters the main a
fresh it was is zero Who was the
elasticity off Residuals are equal variance No auto
correlation off the city was the eggs very
of us And the constituents are uncoordinated The
number of observations must be getting that number
off X The variability in its their values
in positive the immigration moderns historically specified So
these are the assumption we need to take
them We go for lean elevation Now let's
have you got for the consequences of violating
linear irrigation ASM sounds So if you are
going to violate the linear irrigation assumption what
will be the consequence Whenever with Violet any
of Fellini indignation election the revision convention were
produced by oil list will be either biased
Oh variants of instruments will be inputs So
this is the problem You are going to
face me and you're going to violate the
human motivation as I'm sure now let's have
a look at for the limitations off unionization
modeling in data analysis So linear immigration modeling
is one of the ways of specifying cause
effect relationship on predictive relationship In that beat
up on this is variation in the data
well dependent and independent variables It's requests in
a variation in the leader as engine off
deterministic independent variables independent off random It'll this
assumption is applied in many types off signature
ignatz This other rip limitations off many irrigation
modeling in that data analysis Now let's have
a Luca How is this point Different from
linear regression analysis So in the irrigation is
that dust feeding straight lying to don't data
whereas the least where is the objective or
a cost function used to complete the task
Find a straight line that minimizes the some
off spread off the issue Does These are
the other cost functions that could be used
to feed the straight line Minimize some of
this absolute it it for example So this
is how this choir is different from the
near libation analysis Now that's how you how
is hypothesis testing using linear aggregation So hypothesis
testing NVIDIA Innovation can be used for following
reasons if sadistic stand We used to check
whether the parameters off the linear innovation equation
are required to zero or not That is
video The model is appropriate or not The
best can be used to change the significance
off that individual Paige Meters Other methods like
forward selection or backward selection Also use hypothesis
testing for selecting that they give us So
this is how hypothesis testing I using linear
immigration No we'll have a look after you
started with values Infinia Regulation Yes we can
but you must use damage valuables Suppose you
have a categorical variable with key levels You
would assign a categorical variable in my nest
One Nevers for example Suppose that talk about
the value is Geno We just started levers
Then you would have a single categorical variable
You then define to Elevens without binary valuable
it will have a value of zero in
the southern dangerous smell If the subject is
female we assigned a value one This is
properly depend on you that you want to
If you want to make Linda a medic
was to one You can go for it
And if you want to make female because
20 you can go for it It's up
doing in this case the base cases minutes
the regression coefficient off the categorical very billions
of difference in the mean marginal school between
females on years No let's have you back
What are the advantages on did these advantages
off Meniere's patients So Levi indignation is an
extremely simple neither It is very easy and
intuitive to use on understand how person with
only the knowledge off high school mathematics stand
on the side and use it In addition
it walk in the most of the cases
Even it does not feed that detail exactly
We can use it to find the nature
of the relationship between two baby appeals What
is it exactly basis Often any indignation By
its definition linear immigration only models relationship between
defendant on independent variable that linear It assumes
that is a straight line relationship between them
which is incorrect Sometimes linear regression is very
sentenced and sensitive to the animals in the
data Oh you can say it's an outlier
How did these advantages is that we have
a number of parameters than the number off
samples available Then the model starts mortal the
noise rather than the relationship between the valuable
So these are the advantages and disadvantages we
have when we go for many applications Now
let's have a look at what's really linear
irrigation snow formula for example off course lead
some simple immigration given eight that the random
variable on by the response that dependent variable
The slope estimate is given by the ratio
Corbyn's between its and while by the valiant
soft X between the formula we can see
four billion South x and Y divided by
very in soft X This is how we
are going to calculate the medications Now let's
have a look at why Lord Stipulation considered
a linen morning So a lot of deprivation
is considered a generalized linear model because the
outcome always depends on the some of the
input on and cabbages are in the other
worlds Output cannot depend on the product All
who she accept off It's time Lots of
immigration is one of the basic AL popular
algorithm to solve a classifications problem It is
named as lots deprivation because its underlying technique
is quite the same as lenient immigration The
Time Lord Disick is taken from the logic
function that is used in the mated Hoff
classifications on I wanna add one more thing
for loves designation always loved stick immigration walk
with at this point fell right So when
your data cities have displayed take us it
that time you should go for lost equivocation
But when you are data set is a
continual state of safe You should pay for
the booby union regulation Now let's have you
back for the advantageous off Lord stipulation So
lost immigration performs well when the data city
linearly separable not segregationist less prone to overfeeding
but what is over 50 So let me
give you example of noble feeding Well model
walks fine with the training later Maybe you
will get a high accuracy when you are
using training Gator say to young model But
when you are giving up a new nature
to learn model your modern starts giving a
very bad accuracy But why does it Because
your model does not learn from the data
it starts reminding your data This is known
as the over feeding off the model So
this immigration this nonstick regulation is very less
pronto over PT But it also can open
in high dimensional Does it You should consider
regularization that is anyone and l through techniques
who avoid overfeeding in this same articles Lots
of deprivation not only give a major off
how they live in the predator that is
a coefficient size it but it's also direction
off Association Positive or negative Last indignation is
easier to implement Interpret on very efficient toe
train Now let's have a look back for
the desired one days is so flawed Stipulations
We have so main limitations off Love stick
Immigration is the assumption off linearity between Dependent
Variable under independent Vega But in the real
world the big guys really linear Lissa PIB
Most of the time they don't be jumbled
Miss If the number of observation a laser
than the number of features plastic Big nation
should not be used Otherwise it mainly toe
over feet Lost Segregation can only be used
to predict the skipped conscience Therefore the independent
variable off lots deprivation is restricted to the
sweetener West State This restriction itself is problematic
as it is prohibited to the production off
continuous did so These are the these advantages
we have all of the signature patient Now
let's have a look back How do you
interpret on Gordon's installed stigmatization in case off
lots of state regulation We may 6 hour
krakoff value However for the calculation off the
concordance we do not think the cut off
one Gordon's gives on idea about the reliability
off longest application I thought it is not
sufficient to rely solely on it What it
does it takes Everyone's there appears from the
actual data as Yuming Binali Lots stipulation on
checks all the cases where the model has
thrown a probability for one greater than the
probability for at sea Higher depends concordance person
raided The model is now Let's have a
look at What's the difference between lauded on
largest immigration the large is a transformation Largest
implication is that regulation morning the largest transformation
transformer lines to Lord Stick car not stupid
vision fits A lot of stick have to
stave off data but the dependent variable can
only take the value Zero on fun It
can be the realized to feeding Ordina Now
let's have a new cat What does the
biased on the present include Signature vacation So
a lot of segregation The Predators has assumed
to have a linear relationship with the logos
off the success Autumn For example if Vita
one in creation Abbott is three then the
lover would be increased by three on average
for each increasing one unit expert to see
what Tobias some because you're only present simply
say it all excited the resulting your gods
in the biased Um In other words the
bystander is the before locals for the kiss
off all predictors was all he quite a
reference value for categorical predictors So this is
what a biased family present in a lot
of stick regulation Now let's have a Luca
So is there an air Atomic North Pacific
nations Yes Who area Town standing defined for
logistic immigration While love lost our cross entry
it is equal to negative all maximum likelihood
you can use the output off the lost
segregation to classify the data I'm compute the
classifications paid off them You can use either
of them But the second one is Why
is calling Garrity our problem for largest articulation
So basically might alkalinity is a common problem
when estimating pneumonia or generalize models including lots
of separation on cost function It offers fame
their high correlations among variables living toe unreliable
unstable It's seem it's off medication acquisitions Now
let's have a look at what is see
para Middle in Lourdes simplification The trade of
perimeter off lots of segregation that determines the
strength off regularization is called C on higher
values off see correspond to least regularization When
we can specify the regularization function See is
actually the inverse off liberalizations cream you have
No Now let's have a look at how
his proactive modeling using not stipulation So a
lot of segregation is a proactive modeling algorithm
that is used when the Y variable is
finally that Is it time Take on Lee
to values like one or C The goal
is to return by a mathematical equation that
can be used to predict the probability off
even once Now let's have a look at
each stage of selection Methods are baited in
Lord Stipulation The benefit offline SEC regulation is
that it is Parametric on Has regulation coefficients
You're good then use L while our interest
liberalization This penalises more features with one's Nokwe
physicians Everyone takes the absolute some off politicians
while in true pigs the square us come
square Some off beats intro penalizes more penalizes
higher rates so meaning it's encourage more teachers
with similar coefficients Whereas left Elvan could have
a skewed stayed off Who Physicians are few
higher on me Now Let's have a new
pack Why is not stick regulations so important
Because lot of civilization is the building block
off mission learning and understanding It should be
a four state off anyone we starting their
journey in data signs Michelle learning it is
used for violent clashes Classifications the largest function
if off its was goes to one by
one plus exponential off minus squishes the input
on returns and output features between one Hence
it gives the probability off a sample belonging
to a particular class Now let's have a
look at that Second land support Ordina lots
Segregation Yes ran on audience A lot of
deprivation like a multi moughniyah Woodbridge immigration using
more seconds Now let's have a look at
how you inflict improved the precision and recall
off a lot of speculation improving position and
recall Other mixed it after trying my orders
The proper purpose off precision and recall is
not to be You improve our body but
evaluate the modern with better paramilitarism Ation will
usually have better position on critical If you
train with leaves bias and variance he will
help page precision and recall on does better
if France court bail harmony Now let's have
a look at what is long liking in
lots of speculation It is the sum of
the lightly who pursue duels at the court
level The natural long off either patron city
with install created for each multiplied by minus
one on those values are the total is
then used as a bias for patriots Go
into L one on likelihood explosion its financial
off Big one The same calculation can be
applied to one made model that assumes absolutely
no predictive power on a saturated model Assuming
Partick directions the light of values are used
to compare different That's why the Levian says
feast nave on saturated can be used to
determine the predictive power and that is with
a lot of stick regulation Accuracy off the
modern will always different people for that They're
bluffing Peters it But that is not the
case Once a model applied toe another does
it Now let's have a look at How
do you evaluate a lot Stipulation So first
come the model evaluation model Evaluation is a
very important part in any analysts toe Answer
the phone emotions How well does the model
feed that teach predictors are Most important are
the predicted accurate So the following a duct
criterion toe exist The morning performance first day
I see in simple terms E i c
Estimates the relative amount off transformation lost by
and so the list information does the higher
the quality off Um therefore we always pay
for model with minimum years Now comes the
R O C You see what operating characteristics
so are sick are illustrated their diagnosis ability
off a bike It is calculated untreated by
plotting to positive against four positive at various
sexual savings The performance metrics off RC car
is a UC area under car hire The
area under the car made a prediction Power
off somebody now concept in future My tricks
in order to find out how when the
modern does in predicting the target variable we
use our confusion Metrics are classifications brick It
is nothing but a tabular presentation off actual
wasn't credited value which help us to find
a curious Yalta Now let's have a look
at what is the difference between neural network
on Lord Stipulation So Mural eight work and
lots of state regulations at different techniques are
algorithms to do the same thing Gasification off
winter Lost immigration is a status symbol approach
by new related work meaning style brains you
When you think my activation function in neural
network it has like a lot of safety
regulations does not stipulation required independent variables to
be normal distributed No you can give the
political guarantees on more than performance for lots
of stipulation without any assumption on the distribution
off the arguments however it is typically trained
was installed Casting great in pieces on does
benefit from normalising the believers to have the
same standard ignition Otherwise Sud can take much
longer to treat That might be what people
is referring to when the safe Ellen request
formulating Porter that was Now let's have a
look at How do I build with Miss
missing data when running a lot Stipulation not
off Peter may be better than thousands off
Peter points It's safer to ignore if it's
really only take person then to do anything
else all Hughes random forests or something Instead
off the stipulation more the data Thousands off
data points here a severance in the methods
for missing details on they will work reasonably
given only a small portion off Your data
is affected Small later for small later said
friend is going to be hard to learn
from I consider either choosing on more advanced
meter that deals with the problem off missing
data or to actually build the model based
on some sort off booming Which assumptions Now
let's have a look at what is it
Still So it's same sense for support vector
issue It is a supervise mission running I'll
give him that is used for both classifications
and shoot problem However it is more mostly
pay for part gasification problems he can't solve
both Linear on Lonely Your problems I'm walk
When are many practical problems Now let's have
a new pack right Some real life application
off instance So the most used your life
exit application off his is name will be
the introduction from a video Our image Ex
classifications species organization bio inform things are the
most use real life applications off issue Now
let's have a look at for the advantages
off esteem algorithms So is the algorithm has
basically add one days is in terms off
complex sitting force I would like to clear
the wood lost immigration as well as is
them tan form nonlinear decision surface on can
be coupled with the carnal trick If most
immigration can be coupled with Connor then why
use its fear is this is found to
have better performance practically in most cases Is
there miss Computational Cheaper that Oh and square
into Kibera stays number off Support Victor's on
support Vector at those points that lie on
the class margin whereas no certification is in
the so the classifications off is them Depends
on you on a subset off point since
we need to maximize distance between closest points
off to classes that is a key Much
we need to care about Onley are subsidy
off points unlike Lord Stick Regulation Now let's
have a new pact which is the best
funnel for Israel And while it is ah
homogeneous meaning that is only depends on the
magnitude or getting distance between That's not true
for Libya It is universal colors so it
has some very some good approximate capabilities but
particularly for support vector image consistency off support
vector mission on other localized content splice fire
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and decision trees including reduction is over feeding
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multiple trees you reduce the sun chances off
stumbling across a specific classifier that doesn't perform
well because off the relationship between the trade
on deceit as a consequence in almost all
cases land forests are more acute that decision
trees Now let's have a look at what
is an intuitive explanation off a decision tree
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simple One off this someplace machine learning algorithm
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tree algorithm have the same structure Basically it's
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indicted as it as an input to such
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and treat to limit overfeeding as well as
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the trees each best on random sample off
the training data They are typically more accurate
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we construct our decision Tree So decision trees
for customer service play an important role to
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to their support representatives on indicating them Without
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on Super Charles Charge customer expedience Now let's
have a look at how do random forests
on boosting with decision tree compete The prediction
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data on the decision tree will interpret it
as noise Now let's have a look at
what some based tools software to draw a
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of 11 to draw a decision tree but
best for you depends upon your needs The
main challenge in front of business today is
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knowledge on process for this supported president for
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a look at what's a simple explanation off
entropy as using Decision Tree The idea off
in Tropic is to quantify the answer eternity
off the probability distribution with respect to the
possible classifications classes For example imagine you have
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decision tree explanation is straightforward Visitor entries compute
what it's called on information game the idea
being that you can quantify of uncertainty about
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answer Dignity Now let's have a look at
when you one use random forests over Britain
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That output is easy to understand which is
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of the trees on different prediction For same
validation examples note there are some ways to
improve the stability off the traditional decision trees
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don't understand It's always a challenge to explain
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attain million dollar advertising budget in a certain
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dinner London forest now comes in and a
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the instability problems off decision Tree The maker
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have a look at why it's it is
the common did not toe prove those trees
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to the data leading toe No bias but
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generally land of forests would give good performance
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both sad segregation or sampling with replacement alum
with Vanda selection off feature For a sleet
the correlation between the trees or weak learners
would be That means although individual trees would
have high variance But the assembled output will
be appropriate nor variants on lower buys because
the trees are not coordinated Now let's have
a look at how to explain Decision Tree
and Forest Indian Alsthom decision Trees look at
a primary features They may give us inside
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you want to predict whether a patient entering
and year it's highlights on I decision Tree
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old the first fleet on site for over
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off BP We continue our street down to
the branches until we reach our responses high
risk or note For example on a single
branch with me end up with something like
if age over 50 BP over 1 50
temperature above 100 finite then the patient is
highest Then that's a decision tree very interpreting
but not much predictive power oil alone Vanderford
is uses a lot of decision trees See
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different from the others Envy Gator New Patient
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a look at how this vandalization in Brandon
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using our random sample with replacement from pregnancy
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are used for searching for splits this time
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forests which improves the predictive performance Now let's
have a look at for this out of
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a typical value is any Why would be
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algorithm right That beta good When you have
a lot off or a few in fortune
high cardinal t categorical variables Most random forests
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the mean function off the time cities permits
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on its up point Now let's have a
look at Can you deal with music values
in the forest before you replace are removed
the missing values You need to know why
have you have this missing value Wellville How
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the Can you come up with the mixtape
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with the following you could decide which one
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why you have this data missing Then comes
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Why Because you need to do feature extraction
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completely study which you probably dude this feature
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data types to is to use some form
off Present it very presentation Learning off which
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