Partial preview of the text
Download Machine Learning Assignment – M.Tech | PCA, K-Means, Logistic Regression (SOLVED) and more Assignments Machine Learning in PDF only on Docsity!
fNpped Machine Lenanieg 96 23SL001F oO. © ae [ ® ‘| a) healoke Egeovnlves : voles, we cove “He chanackentshe equation: To Rot tre age dok (Arar) sD. Ves leade ~to: Ps al r) “l aed 4 =o 0 \ \-> Cepanding ste, dakeamerant + Carll 9) (lea) -\|- ie lesan “0 +0 20 (\- (>) (evy-i] e OY LOSO-a] oe (i=) [eon “\- Cra) ve (1-9) [oe ery 17) co ayer) (tea) -2| =o (1-a) (2° ~atNHZ) PO, (tea) (A =2a7) 7° C1-aA) (8-34) 2° reBan-waBw =o +H Bd ee » (-xrah a3) =O — TaHA-3 FO SreUne B72 Co aR LS) ye 3 they A143 eo . “Gigen valves ane | rAyee mr =) d3 23 reo sO b) olen petetion Ww RCAr coertet 7 Exggovedins neprescak Ave \eriante cophsved by eoch proupel ceanpecent: . igre etgpn valves Weans Move voxant explored boy ste. grouped Comporent: Rajoksons behest Coyenveluce ond Mahiveds & Sam ck Eigeavalves = Tran. & A Tac (A) = tarel = 4 sum & Bqen yober o OFITB=Y 2 Produk ob Eyenvelver = Detrotrank at A dok (A) = O Grodock ekGe walwes Se xiKny lo $0, BEC@Y ao , whds Bsc wears erabix A ES StAgulan (nonQoventtble! Q Toke hracked Mewwers 2 Joo Model predicted bayers 2 %50 Boe Bsckves CTP) F200 Cwodek predicted bo yers % they porchoses) Face. Postkves CFP) = (50 (mode) preaided buyers but ty eda’ perebese) rnodeh predicked non, ~ buyers & [00 —BS0 «650 “Fre Nejotves (TN) © See Crrocdeh predicted Men cLwen and they d2dnts pevakose | Fadse. Negatives CAN) 2150 Crodel predtebed noncboyer 1 bak atrey cxchualty porcboted dekvol Prtwol hon Royen Royer Tote Pore drcked Boyer TP 2200 FPs\so R50 Peewee Be EN= {$0 TN os cH (4) 43 43 Be 24 cy (2 a 2.03 242 2:55 19 C) 1 —— : su] 180 $F Ek6 a (4 ne) —— ieeE) (9) GekP Gye C9) _ y \ 4 <2 06833 G25 06938 2+ OSES Om 3 “Vs ~ 833 228 3-350 2 Was 3 6 -oS wi6a Ons 4 3618 ~ or S8BS 4 y os 6.833 0-25 Or69°3SB - OTH16S 6 i) Vs 2169 22s by 6188 83-2508 6 5 a5 olga 62s 010298 Oulas geen ZY xbeR LOW” s (xk) OY) = = Ine oe bys Ss eens Ve 4833 = lersse = 3 x) (xe) 13S Cov (x) > 2) Ce e SF 3S nel Grl cov (24) * g(x FD) og FE ete 6-\ n- 10°33) 2 alee cw Hd) = £¢7-DO-D) n-\ aS ts | Ce Vis ates & velwes FC \e- AT\=0 BS is =) Ss a\eer> | 72 dy (anee-a) -~ (sco (es 266a te — 22F = 0 q°58\ - BSP New 5.666 A+ 5:33) FO dye HABE hoe ENDY 4 oan volvey Exon weckors of C2 [BS bs ue wet ®\ = 44436 Vs DNbG cv, 2AM BS WS my oy . as hy hs Deb 4 at [ | Qs mts) = tees on —O) psy a 1664, = e4AHY, ——O rom © PSY, = bua BSR Esy,e OFF —) fom @) DA GEY EAT TY pss, e ahaay - 2l6ey, 5% = 23084, —@) Rom equ @) teks ogsume LF ota ws s[Rbowe foun) ' O-6h4 (ya Coeaaye ate Mie | feet 0'6uF ech Vt 202 , NTs CES) bby tlqay Nie Or R388 OSU UY us = 0644 Now Ser vallucs oo, XM, 2 eH 93¢ Ao” Rtas O-P 383 Bg weecows = “| oan | -SUS\ va [O54 -O'8383 4 (08.388) 4 Co. Sergey ) 0-8 BRR 2 (0-938) 4 3 Co-Strteey) Stan) 4 ¢ [rn a id (0 s444) 4 (oBse)+ , (> Sue) S(OP396) 4 9 (ost b(C-e3eg), 5 (0-suugy any KR 3-016 3-310 S492 $. $32 3: O04 3.95h y L é a urivesity conducted ca Shody fo understond We ~oodion shop bebween We. number of prockte desks comnpleked ork “te. probability a poses} Ye ooh exon . & toqikks meyestinn prodea wos Aatted, and Ne pone melons chkoined were Bors and Bi = og, Catolodcd fhe probaitthy frok a shodenh ME paws fle fecal enor R whey complete : aS 9 pretite. eds b) e Precktce tests { tt @ CBF EOD legpictee Reqrestion Modal P (Poss 2 where : Bor -5 Cintercapt \, Biz OF Ceceppretent for the timber di. prochice, tests) cise pamber of pyrockfee cfeske Corapreted. a) Probabitthy of poetry 2 prackten Ksb ore completed CX-3) Bete 2 eSH@ERB) aS BY on26 ' pts) >t et \+ eo Irene (#13646 3h + & e-oer fo .463'% an b) Prrabyabitthy f- pasirg €e prache cess one ene Completed Cue8) Bot Bix se -S+oR(s) > HStGu Thy e ~——. a pt = Li x O-gor pe Poss) eer 2 inert 14 O26 246 ors Fors. o