Open Codezhak opened 4 years ago
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.
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@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 1 0:0.03694541 1:0.071669891 2:-0.092664048 3:-0.064463556 4:0.040241361 5:-0.024055684 6:-0.050895944 7:-0.12840363 8:0.012196725 9:0.0043732212 10:-0.023462353 11:0.097336009 12:0.018651906 13:-0.019849913 14:0.12040991 15:-0.047160998 16:-0.0051556467 17:-0.1239234 18:0.094275281 19:0.025820591 20:0.093659908 21:-0.074600518 22:0.063219473 23:0.079827301 24:0.0051653702 25:0.10770133 26:0.0073127155 27:-0.083744101 28:0.10648216 29:0.20111726 30:0.0022260793 31:0.085927598 32:-0.15429094 33:-0.15328857 34:-0.11767324 35:0.15361258 36:0.090266727 37:0.029450133 38:-0.0015768503 39:-0.027409237 40:-0.041641694 41:-0.076887839 42:-0.046940949 43:-0.06171843 44:-0.10300581 45:0.031150701 46:0.050628506 47:0.038913436 48:-0.029119743 49:-0.0057133124 50:-0.048775818 51:0.21805395 52:0.15209258 53:-0.024650376 54:0.073181532 55:-0.050327212 56:-0.010669088 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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
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
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.