intro - PREDICTIVE LEARNING y = “response/output”...

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Unformatted text preview: PREDICTIVE LEARNING y = “response/output” variable (unknown) X : (3:1,:132,- - 33377,) = “input/predictor" variables Prediction: Q = F(X): L(y, F) : loss criterion: regression: L(y, F) : (y — F)2, |y —— Fl classification: 3;, F E {C1,C2,- - -, CK} L(y, F) = Ly’F : K x K matrix Lack of accuracy ("risk"): R(F) = EXyL(y, Optimal (“target”) function: F* = arg minF R(F) Learning: T : {xi-5.1%}? “training” sample F(x) 2 learning procedure (T) r: F*(X) F¥(§) : H imcagt @mcfiz'm” GOOJ : ‘QK'MJ C6004 Ctpr to F¥C£) LASng {ha 010qu A FCE) zbmmé WQMijLJEL‘ETI) mks coax/336.: AAko flwimg Wmmx ’W‘W‘LT OJ;- Lovegtck um CAEUQLOPQJ owe/x Q'Le [613- C1\ Mucmme lewim% (A: 24:3) - becisim Mm & fl.th boa/max CL) PS‘QC-ULOlO%L/k _ Macho} mesh (35 Em%imemim% - pat-tam Mcog, me am mei 35in by; mexfinools CLMS+M1M$ (Lt) El-OlOrévl — gush/dang Theo/M" ,ueAvl 41AM. 9M flame mewoois. (1) Moch 44Aqu Cleve/toch 41M ,Qa'mea/L mudmw Q 90% {Hamlin/Lew; flemfl) (3) MMCM aioou't Ame/(0W1 x JKvaoiov-Leot (MM wig Wee/1% (Quasi x MMLL'MQM) FQLLfla—ys-i-em ""J U—H RemlLJcI/l COW [M abut-fie 0M£4mwt g‘m \oo‘H—k 61%. him—Wimd hapleiojflqfi Mos-t kchMiCLueA MO'El-vog-l-ed WM hemichl'cg bis P1123 clown/n.5, ‘I‘LO we COMJ/fia/‘H/l (:L} (L) (Q) Li U0 memo“ = mace-IQ bullet” MO Mathtflcfl Mt'UE/XSQU’LI wow oww( owe/1(Aeomavmlole) (ML: haul-oi Uo(p€At,’ U0 42433 [OLMCH TLL‘flS Each maimed how Llodlé 0-? 75521595215 JFMMc-ELMS F‘Qf)J ample x3551 U1 [5.1%IY10LQ / mol'se Aodu'o cg: Fags) +5 1“ ' ’1".— mode“ 5,394 “3&1ch H45 Joe/0‘1: Hour +0 choose; ME‘HAUOLS oki‘y'é-M Cm Mat Werq MAME about F‘cg) MorHH flue—9140A +0 ‘UJJA-Gi A?) Madam about We rumble/M off; ham/1.4 T/‘wt oeuehal —- mtg/milk be»on on use comm-[thee Com aim-3cm. Com-EEM: Repm+eol (ampml'oal) ISM-(:04qu CWPMlSGVl-E be+mem find-(440015. must be Jere/xwied mm (la/L12 U0 awe—Hand dmml‘mmJ—BA Oi’l/LULS owe/2. OLH AK+MOL£LEJVLJ Fm euuq xvi/LE-f’I/loMJ mm w an: leb‘t x.)ng ASL-MDHPL‘QVLS (faAaaE $mctifl‘4, 3/“, flow/trait? final) ¥0A wealth LIE A34) MHCIQLIL/i aPMop/liaie (amok comumsatq) PM‘CMMOWIUI Amtl'mgjs av: Pan-Hca[m ,wamplveA .ro‘aoulci mo‘t '04 ixMaPolakvr loetéo'hd flose fipecixcl'c; .QXa/Mloleo. alSo _ (AW Wu? = Amdfiwt umtqble} This M MEECIQHEI hue LG #145 OlM‘H/loJllS (Mew) Ame—Puma AA and O 3* “We CUM Petft 0’13. Se(ec~l:l'ovl Maxim; W056 904 (Jo—€th it 010645 beat W (mo/m. libiqu +0 [08. Nate“ (meme :2) mo pape/L) No idea. 0-?- Mouj' «mo-moi WW {:Aied 4:0 céei chose/n WW‘EEA fiHlaA—af. Paper; Mlecfiim eJH-"ec-L— (mo Papa/z. [{- mot 1149 but) H 0104 PM +MMI'Mé (on "~leo‘tuoianaJ wat Ludo: IM aunt/l aka—HR Moutt/lSiS’ view 04 {Qt/[LCM dePQMM-‘S U‘n ‘HAE’ qufiist ‘ MM Miss/th malt um We. memod- Au-H/la/i is mama .Qx rth M applgfms ‘I’VLQL/‘L Own Mae‘l’hod mom comps-fifty“ ._ Mme AMEN M tom/11:14 A W049, {mothqu/Q +0 0&0 _. m+meak Wmtitmafi MmV-‘Wt tmoaqts +0 be. beam/vi Commutation/13: Comaomiscmx most M56441: Omlv] meOSE’. 0‘9 -H4e Papal 04.4mm“) have mo UJE/CH-EM MMeo-H, Ana-’1 olf—F-FeAmffid ,aKl'I/s Wow/Lg #443 Cowpe‘éffflf C Som‘éa Fe WPQEEELEM) whwfiifi‘fie 0m: CO‘MPML'SOVLS mow/13 0142/2, MQ‘HAOO/Ls morl- ALVL—I-P/LLO‘E'l/ng C 012(2ch —-*> Qm't (m eguaf Que—EI'Ma) Campanm£s o4 Lemml'ma Aigmtlfltms C HOWth Moxanqu 3— SKI/WWW) 1- Model 0}}. [Gaffe/Um AJAuc—Emaz EKWPIES: OLR 3‘ 3 am Jim/team «Ramada/L5 A an F3ng :OLB+_Z.C£A'K<S V (fling) a“ l/AloLle-‘EWE Wee/talfmég: SVMI 3’ I mtl Polémmu‘xlg +0 4:;er MJM C cAe-LQJIM/Limeq ——-""'" 11.. Same ‘PMMC'EiM I Que/(w (flack 0+1 CBUCLLI‘EU‘ 0-f- rvaoc/(QJ ‘Eo oion‘l'q PoEuAmkimx: EQEIJC32F%5H COL [fl-MOL+£.\ bm‘EDL 1 Acme+53m<aa C Mos-E. "Ma—Pmd ") OLR m ‘03P (Cg—0.0“.Zaaxd )1 u 3‘ l M tied-CL EEC%R“QB—ZQAXA&)Z ézl rRR/LASSD M )xiflf deem: 0LR+{ 22‘ AZIle 3:: SUM 400$) 3.13:5 #Cfiji i AIW FCXLX) l M A M 2-. olw’rrli FZEi-(QL FC§L\]++>\Z'QS A"; 93‘ .-—-"—'_ 1A Olefimféaxfifm flea/1c.“ MQ'I’QOMI Ml-MEM£5,€ (Mo—+01 g‘n Amie/c? €XWP1€Q _' OLE‘. ‘ [email protected] Mowhix 094%qu RR “ Lt LASSO -' (Zuadhafic {meta/LN SVM - H OLH couueK whims WWI-“chute Mfrs/\{rwuwt l alonl'fil Mane @lexlble mange/{meal (TAeeSJ memd mats) chsJ-Ml'mg) Hewukfil'c. MMCLI Ahqtegfeé C ,Lflwle'ue. dwiflam) Gheedg} fi+eava~e¢£ OALQ—éCfl/vbfi EM. afiWIWM ovo‘L—l'mi‘éafim Multt-EI'E. M41.M.1/MQ QKWI‘EHML‘C + /.)+CL'(:LIS"£:lIC£L[ PAoLie/Mg Aol’rv‘t Mepwc/(s CM ,o-l‘a/l‘b (9.9) S‘i‘a‘EIIS'Eicoa-I WPM'EIQ) 04er m Aeahcta «MACH-631 ax: {pd-Q aA I. &_Tf__ PMMEMS Cam't \oe mpmaled ab Ale. o—Sl-Eeah done Ala/L Wash”; 084% mmogafie COMOOWQH MLMI'M my,“ tag-be Mm WW1 Aeqnofa Smmm Mal — ,QQMML‘M% Macadam—602 1- Mac/{eel on. Pat£eAM 434Ach I _ A 5%" "‘35 “mm—{Jan {901° «C M 151(9):.fiZAC‘gMchLJgH 01.50412 1.31 +).PC9.) Ame—“@416 A, = L I 53-94% mo-{r E jag-“Col o. :: WWI/v1. S Cg) CR, -—3 ..-. 13(vath pwcedmws cam Dawn ,4)“ owl/I 04 0:1? (II) alcove ...
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intro - PREDICTIVE LEARNING y = “response/output”...

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