Deepak D. - Home · 2019. 12. 18. · Deepak D. - Home
Transcript of Deepak D. - Home · 2019. 12. 18. · Deepak D. - Home
-
0usnon M, SO[gtr'ons - Dtc. zo9 | Jdn.11tg Machine Learning $ rscszl
4a' For *At '*rnnsn-cti"or"rs SMctn i,41 +rq +ohJa- hnpuh ttwt'Urui^t'
(il tl*op6 o; +r^r * \ transozrion ettr-tdr \ +t +^hlLurirr'r ffr{p{.f -b Ufr}kiir^fifrn
tii) who^r u.lL +h{- ^y^rli- W t fl.r o,^d.aL fulfrtlrrL"fo +h{ $wxntttpnt 1 {4.t .f*U t
€ntvoyVcs) = - po [uJ " p@ F p- tr.g, po
Goin I s, D = cntropJ Cs) - L tu*,#| €ntrory (s,)
ti) H,u'-, -l{',u,- o,. q vn\tom/^ oLrrrF 4 OJL po Si-tU-lt irrultwn-ccl.p 5 Cur NtXatiroc 'wfianttt
-= 0.qq\--------a
eo,
+
)e
X UtlrtilL
linShanrr L 3 + S 6 4 g q&r T T T F F F F T FAt T T F l= T T F F 't-
Taurr dnw + t -t- +
Deepak D. , Asst. Professor, Dept. of GS&E, Ganara Engineerins c;ir;g", Mangaluru
-
Outs+,,on ftau golunons-Dr .2olg I ran.zolg Machine Learning $ rscszr
0t rn nn*rp g- ,5jd
hd *tr^" t^j"'rp[ {rr aL, Lt-., lsu| €nrvop,:cs")f-r@|: +tt"ge"(+) -HQ,H)] +
+Ft+)tr,c) -t+) t:'[eI0.J6os t 0_fofo0 '+615 qul Sbifi/t irt, qatn twlrudo::: v
qo,n [s, u) = cnfropy c') ; ;];*,, ff t*ro1cs,J
ll tt-tz'l
-ryJhrgu UN9
CLz +T L 3F L 2_
# QL ;t',
Unlrrta
lhd +frc ffipy# €nrrolcrcsrJ
= 0"qQlo - 0.+61s.
ofio,ihilfr 0.o
Deepak D. , Asst. Professor, Dept. of GS&E, Ganara Engineering Gollege, Mangaluru0z
-
- eK3)
br, H -(+) rry,(*r . rr /'.' quol t\0.$ *Vc
irntnnfq
Iv', Qoi.(1 ,01= €ntuop6(t - Z
ve uo,lrur (a): 0"qqO- 0"q938
in hp.narron0.ssq+ t0.++++0.q838 S*s+ian-:=-
Clsrrrt dolru&
'[ €*oyyCs')
Deepak D. r Asst. Professor, Dept. of GS&E, Ganara Engineering Goltege, Mangaluru
-
Qurs1on Qa+a gohnon - Dtc ,totg I 1ao.zolq Machine Learning $ rscszr
X. C, bns;clur- n Mboll W b6uuw hn ^!fl.t tqn/ffii:Tq.0m0 o,nd Tt-a,nL, (ulposr Tenrn} tpinx q57' 4 +hL 4+nL
0/nd Ttnnnf winr +h{" *r*t*V n11a'u, Srno'9 *ta W tfllnux Ttnnl, ry 3o'1" t -t/rt,n Lsw" forrtout Uutt *,rld - 9n +{,{- Atfiul,o,,rd, ?s ./.ftn'nl ntu lbfnirrv) *@ fW *
p%"t clyr Te.amL'g
nW vUrfii,tt $tl\sYrw, q Tu^!, A
fu host {fu wt-t rnofuA bch))Len +fu firrn "tln/ryrs I uth;nhtrnrn wiil rnost J*% wrw\t 04 +tu u)vnnur ?
€41*'+ probab&{ -liut Tmrn0 whs r^+ probobiJ./q '+{.oJ twnt uvns 14
*, Probo.billfrJ f-hot ]1tnn!, l.ost a +r.eLt hod uron i4 p CXr[ Vn)
+ probabi{^& -{+'of rrnml l"\lgtrd +t0 nNLt\won bJ Tapn'O i^ p[Xr
I VJ = e.1e
Jto F on{lrno Corri be- !,,{vod \ ulrr.uoui;,$4*P/rl I ' L ,. rr ,^ U' L y, I X,) , wtirh iA {f" tnrtonlwrwl, "probob W
+l"Ot TtCI,ml wfrrZ +{"{- nur nafuh itr hreU"
o/rr \ -' It'LYoJ = 0'q5Plr,t \ r nlrt \r(Itl= 7 fLYil
= j * 0"q[= 9:g:
rn0fch
= 0'?5,-.
-
fo 'sno-m-g4rn SoJ*hh n* ; nssr^CIl B, l.f*ft *gt g*-
- .". Y :*il"--'.tr ::i :q-[ i ::]:-
Ustntr B%u -lhmrurn ,
pIv' x,) - P(*,lvJ x r(v)
rtvl x,)
P (v, lr,)
PCX.)
P [x, lv,),r p [v,) + p[x, lyo) p tv,)o.?5 * o.oS
( o'1s * o.os) t ( o" Zox 0"qt- 0.l16t-= 1 p^ | r-r- ,[Y, Ix) = !,-O.ll6L= 0.8838
ginct P(v, l*,) { P(Y, l^)
iTtntn0 hoJ fr b,hr probab,.{rQ q wirnnirna -tfu1, TnnL,,k=:-fltrT
N obo ' tlu", +1.{.r. orr frr,, ujmfi - hjin I hosy .O wrn@ fiosc
-
wsriol P, Machine Learning
8. C The following table gives data set about stolen vehicles. Using Naive Bayes classifierclassif,ithe new data {Color: Red, Type: SUV, Origin:Domestic}
Color Tvpe Orisin StolenRed Snorts Domestic YesRed Soorts Domestic NoRed Snorts Domest Yes
Yel ow Soorts Domest NoYel ow Sports Imported YesYel ow SUV Imported NoYel ow SUV Imported YesYel ow SUV Domestic No
Red SUV Imported NoRed Sports Imported Yes
y: cugryir pty) f o(xiIy)- ley -i,r(toTia\^fiI , ttatut) + tofrr I fA ,y,iln,^,)
UuT
(tlut lsPo,ts , surr)t Orir4h I Dor,sric , gnpotc,A)
l, stalrn 'Vo)=l(tultn = M)"
srolcn,y.r) =
Stotcn = N, '
\lnlrru
p ( color,
P krlnt "P ( ult"PCt tlr=
0-6
0"t
=0.t= 0,6
trls = o.g,lt - 0,+l/r - 0.1-3/r'0.6
e.af stot,n =!u) = * =P.llstotcn 'r,b)= +Y,tbrrl I Stoten:!u):
YrLln*l Stot.n " ^u)"
2^
s?
TP[!P" = sPorrP h[p" = cPorr
P($p. = suv I
P[rgp. = suu I
qrJ
Color Yu NLOPu{ 5 2-!ttl-0, L 2J
Typo Yu AfO
Cport + g-Suv I 3
Deepak D. , Asst. Professor, Dept. of GS&E, canara. Engineering colleg;; Mangaluru
-
Q,urStioo Pa{,l 9{rmOoS - Dq,.ZOn I Ian.ZOn Machine Learning f; rscszr
+
4
Val.lrt
(Lass,tr, +i.e r\xrt) dofc = ( gra, Sut/ , Dorn uttL)
For SO(rn = yg ,o
( nLor. Rtd Ismt,n,y,{)* (tp,= euv f sdrn ,y,t)xptv u)
+ 0.6 * 0.2 4 0.+ * 0"s:) p 02+-,( fo, [tu.(cn = Alo :
€oll\rl+ [cobr = ?d[ sml,n "ru,)*
(Trr* Suu {sml,n'No)x (rnl'^ "}em(shir{ttt,n" ruo) x {rur)/\Jl
-) o.+*0.6{ o-6+ o.r-) 0. 04L
-
Tht,r0rioin Au Al,
Dorn'6 gi1 z 3?mportud 3 2_
So, orlc tloutd UMA',14 *?rt fW dninb)
P [Otig in ' Dorn csnrf.f+otrn = U4= 2/s = 0,*P (oriSin= Dornunc I vuttn= M) = 3lr = 0'6
p [oriX.n =L^prrt",r I stalt n=y,J)= s/E = 0.6
P [origi.,= ],npo,ttd I Stot,tt = I = zlr = 0.+
(ot;Xin = Domum I
S'{rrttn. Yri)*
M nn|c Sfuttn
Deepak D. r Asst. Professor, Dept. of GS&E, Ganara Engineering Cofiege, Mangaluru