SVM Prediction Accuracy issue

[Codezhak]

Hi,
I am using Libsvm for my face recognition application, where I need to detect the person which does not match also.So i used predict with probability feature and I am getting predict_label (always 1) and target_label as different at (if(predict_label == target_label)).
Can you please help me how accuracy is calculated.

[cjlin1]

If all predicted labels are 1, then it means the model isn't good.. Did you check cross validation accuracy?

…

On 2019-10-10 05:21, SHAJESH wrote: Hi, I am using Libsvm for my face recognition application, where I need to detect the person which does not match also.So i used predict with probability feature and I am getting predict_label (always 1) and target_label as different at (if(predict_label == target_label)). Can you please help me how accuracy is calculated. -- You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub [1], or unsubscribe [2]. [ { ***@***.***": "http://schema.org", ***@***.***": "EmailMessage", "potentialAction": { ***@***.***": "ViewAction", "target": "#155?email_source=notifications\u0026email_token=ABI3BHT3G3S6J6P2BON7KHDQN4M3VA5CNFSM4I7MDUZKYY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4HQ5IGGQ", "url": "#155?email_source=notifications\u0026email_token=ABI3BHT3G3S6J6P2BON7KHDQN4M3VA5CNFSM4I7MDUZKYY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4HQ5IGGQ", "name": "View Issue" }, "description": "View this Issue on GitHub", "publisher": { ***@***.***": "Organization", "name": "GitHub", "url": "https://github.com" } } ] Links: ------ [1] #155?email_source=notifications&email_token=ABI3BHT3G3S6J6P2BON7KHDQN4M3VA5CNFSM4I7MDUZKYY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4HQ5IGGQ [2] https://github.com/notifications/unsubscribe-auth/ABI3BHRNIKV6ACAQL7ZNCSTQN4M3VANCNFSM4I7MDUZA

[Codezhak]

@cjlin1 Thank you for replying,
Sorry I edited below comment that I am getting Target_label 1 always.
In my case Sample train model data is :

svm_type c_svc
kernel_type linear
nr_class 2
total_sv 7
rho 0.0297609
label 1 2
probA -2.95891
probB -0.00770355
nr_sv 3 4
SV
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-0.3045621388886415 0:0.13265789 1:0.04287947 2:-0.022533843 3:-0.029676065 4:0.13877369 5:0.093323424 6:-0.16009481 7:0.14464186 8:0.078488223 9:-0.019689847 10:-0.047618113 11:0.050374396 12:-0.026399303 13:0.03577666 14:-0.064175278 15:-0.0031519399 16:0.030996069 17:-0.012105824 18:0.074721694 19:-0.13510875 20:0.034691051 21:0.044050481 22:-0.024635581 23:0.023730418 24:-0.15296528 25:0.18736264 26:-0.016271692 27:-0.026707249 28:0.18839835 29:0.071683466 30:-0.029572923 31:-0.0069101928 32:0.03177242 33:-0.10820068 34:-0.056612954 35:-0.066101216 36:0.024351677 37:0.1544853 38:-0.095620312 39:0.049340796 40:0.062288772 41:-0.018870469 42:-0.10460676 43:-0.12368365 44:0.063146904 45:-0.0096856831 46:-0.055579446 47:0.01499547 48:-0.059010319 49:-0.020819254 50:0.022097986 51:0.13511468 52:-0.061164215 53:-0.084607273 54:0.024798114 55:-0.115839 56:-0.12259501 57:0.022010641 58:0.10695752 59:-0.023524566 60:-0.017427064 61:-0.12032808 62:-0.045925461 63:0.032045227 64:0.020688491 65:-0.17340675 66:0.098170549 67:0.065298922 68:0.077701703 69:-0.020727865 70:-0.043914281 71:0.094675548 72:0.086765252 73:-0.16631667 74:-0.0040651988 75:-0.14209117 76:0.028333241 77:0.040182944 78:-0.071837462 79:0.086242184 80:-0.038932201 81:-0.01737581 82:-0.01370337 83:-0.25589895 84:-0.0022302177 85:0.22972676 86:0.021776997 87:-0.18178098 88:-0.043261051 89:0.0097726109 90:-0.056673307 91:0.16500759 92:0.085731834 93:0.026602793 94:0.11517385 95:-0.099840604 96:-0.16715655 97:0.024331942 98:-0.067629561 99:-0.067295313 100:0.053321753 101:0.066249423 102:0.050632149 103:-0.040784474 104:-0.11061683 105:-0.010278202 106:0.080082864 107:-0.0037509578 108:-0.095562667 109:-0.15826115 110:-0.085019626 111:-0.059350546 112:0.11749499 113:0.024804683 114:0.10132024 115:0.0097814417 116:-0.031180723 117:-0.015148983 118:0.1490718 119:0.041099191 120:0.11612652 121:-0.016521929 122:0.11280422 123:-0.013151463 124:-0.093105428 125:0.051658563 126:-0.12643787 127:-0.057971671

Sample testing file is :

1 0:0.08869078010320663 1:0.11265850812196732 2:-0.08408842980861664 3:-0.10734429955482483 4:0.09569554775953293 5:-0.04100192338228226 6:0.034704867750406265 7:-0.10822495073080063 8:0.012405091896653175 9:-0.029127100482583046 10:0.0064252461306750774 11:0.05538499355316162 12:0.04487818852066994 13:-0.028461096808314323 14:0.08426535129547119 15:-0.06002195551991463 16:0.06701210141181946 17:-0.1009158119559288 18:0.12260399013757706 19:-0.01649876870214939 20:0.06636760383844376 21:-0.020320240408182144 22:0.02745750918984413 23:0.06801094114780426 24:-0.022455191239714622 25:0.17613570392131805 26:0.03230143338441849 27:-0.07424364238977432 28:0.09025981277227402 29:0.25953787565231323 30:-0.03251327574253082 31:0.044713057577610016 32:-0.12395288795232773 33:-0.1393536627292633 34:-0.08701413869857788 35:0.23229508101940155 36:0.058736104518175125 37:0.04533463716506958 38:-0.09867251664400101 39:0.009782051667571068 40:-0.030127104371786118 41:-0.11337379366159439 42:-0.06302675604820251 43:-0.07429847121238708 44:-0.009119205176830292 45:-0.019430872052907944 46:-0.006596052553504705 47:0.07191171497106552 48:-0.04393550381064415 49:0.040151696652173996 50:0.01908142678439617 51:0.1764426976442337 52:0.10995174199342728 53:-0.064067043364048 54:-0.004895928781479597 55:-0.06563697010278702 56:0.08175726979970932 57:0.16051332652568817 58:0.1841152012348175 59:0.11843384802341461 60:0.13972434401512146 61:-0.007530031260102987 62:-0.13817663490772247 63:-0.1228240355849266 64:0.056343164294958115 65:-0.03253542259335518 66:-0.00896674208343029 67:0.018803920596837997 68:0.07428935170173645 69:-0.038438934832811356 70:0.01932407356798649 71:-0.017723485827445984 72:0.1281740367412567 73:0.02006290853023529 74:1.9832190446322784E-5 75:-0.0075545888394117355 76:-0.03605575114488602 77:0.05641820654273033 78:-0.10415695607662201 79:-0.001241249730810523 80:0.09665007889270782 81:-0.0515027679502964 82:0.05948938429355621 83:-0.08536400645971298 84:-0.07551930099725723 85:-0.07200975716114044 86:0.05448233708739281 87:-0.06424335390329361 88:0.0023951532784849405 89:-0.08267461508512497 90:-0.060305897146463394 91:0.09392296522855759 92:0.17346027493476868 93:-0.05862409621477127 94:-0.04794041067361832 95:-0.09105176478624344 96:-0.11403479427099228 97:0.04632284864783287 98:0.0655788704752922 99:-0.1020464226603508 100:0.03114120289683342 101:-1.2855100794695318E-4 102:0.01882193237543106 103:0.018400922417640686 104:-0.04462011530995369 105:-0.245097354054451 106:-0.07447024434804916 107:0.08699245750904083 108:-0.003961259964853525 109:-0.020035304129123688 110:-0.16071002185344696 111:-0.035762544721364975 112:0.06791653484106064 113:-0.16253145039081573 114:0.0739869624376297 115:-0.06037476286292076 116:2.73046171059832E-4 117:0.1137760728597641 118:0.0011637017596513033 119:-0.11534024029970169 120:-0.07720830291509628 121:0.20741181075572968 122:0.08804057538509369 123:0.025654399767518044 124:0.12709087133407593 125:0.07892488688230515 126:-0.05567362904548645 127:-0.008171903900802135

My doubt is in testing file there is only one data (one line) so index is 1, so I am getting target_label as 1 every time. And in my case in model file (there is data of 2 persons ), the index of target face is 2,
and so getting predict_label as 2 (which is correct), but in code (svm_predict.java), at if clause if(predict_label == target_label) ++correct; , variable correct never get incremented..

So accuracy is always 0.0%;

Please Help me in this case

[Codezhak]

Hi,

Could you please help me in this case. I am attaching the predict output data below. Could you please give me an idea about these values.

labels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

69.0 0.009766350027215745 0.008718788435991671 0.008714248367779589 0.01009612910406905 0.008810651760798765 0.007199669815463763 0.011279611860364866 0.007876555804444537 0.008771739007047094 0.010226308566184304 0.007987598090904446 0.013106959757897264 0.012791730642928609 0.013306778616485385 0.010275457089693834 0.010381747063016636 0.01048066547401133 0.012130526059080481 0.011596904443943094 0.008525880016375508 0.013414159773918148 0.011080507534170378 0.010220942129122727 0.011335087843791102 0.012599536513399808 0.007963997885801002 0.011967775983568104 0.010809823695305287 0.008077167983766769 0.009239526561179642 0.011225212243597642 0.009698110646958864 0.012332212996859214 0.0016028208496402278 0.010039914982045957 0.013777257243892135 0.008441028976934514 0.0087232354065756 0.009852114786750818 0.010543586117699196 0.014802952166708195 0.013204612745489714 0.01105621159172587 0.015241204046930735 0.011400324980291008 0.011703140846543602 0.005625613086914375 0.01330125846852294 0.012927454682873481 0.010569029308808374 0.005529270401969083 0.013183109915957053 0.010996940370459424 0.013776714701248008 0.010684849080162637 0.012601060135684591 0.009348879873881624 0.0074177779597208535 0.01099654683054148 0.006905452504926945 0.012869029339493254 0.011889579446811767 8.114384939217007E-4 0.012949375124371191 9.99447613272359E-4 0.009111369709154448 0.013002952980131162 0.009118058694081534 0.02296124258017252 0.007067531681322627 0.010405418275145323 0.009865121234733812 0.009910737045806908 0.00995742795217193 0.009089866311820006 0.010290719239926034 7.396414939486157E-4 0.010983618119280952 0.009701946614437403 0.009865429548498493 0.011044470061753736 0.007626160404856159 0.011514783926442175 0.010377611151098002 0.010029471694536468 0.017120699872452055 0.009992492331929706 0.00956459869570664 0.01148371498765532 0.012745678554922937 0.018075933514996353 8.351298078053558E-4 0.022526354673576 0.013679062638080873 0.014729257738683164 8.039045889690222E-4