Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7533291356645115 0.35421395076985074 age Hidden Weights: 0.022230836818651234 0.7390156300347646 0.7053387476131181 -0.6615930936277854 >50K <=50K Error sum for iteration 100 = 56.53903077212294 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7534190389057981 0.2575225599433837 age Hidden Weights: -0.3985565572118925 1.2929288539669312 0.2962230214597048 -0.1194084404399498 >50K <=50K Error sum for iteration 200 = 60.95798426437753 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7535056857848805 0.048953666731644474 age Hidden Weights: -0.496619941622068 1.429088741377744 0.6725940425318022 -0.5420051252831414 >50K <=50K Error sum for iteration 300 = 62.61371157690109 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7535925210434736 0.054720417369823984 age Hidden Weights: -0.6266145995700053 1.581612169822905 0.9641114222961011 -0.8965485721591911 >50K <=50K Error sum for iteration 400 = 63.45167591033083 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.753669929057377 0.0719980232344843 age Hidden Weights: -0.798073811890285 1.7625456213406083 1.3542841369412832 -1.3117000442253661 >50K <=50K Error sum for iteration 500 = 63.557041247887916 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537292175231811 0.10337876323632655 age Hidden Weights: -0.9353556369449375 1.9060934530801588 1.6666530038425944 -1.6394886884011326 >50K <=50K Error sum for iteration 600 = 63.204112865141816 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537698351681479 0.13600452354315717 age Hidden Weights: -1.0400164030546994 2.014706679502338 1.896127705704513 -1.8783977005359496 >50K <=50K Error sum for iteration 700 = 62.75241597440773 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537962031368544 0.16143751797449113 age Hidden Weights: -1.1165484685064917 2.0937204927837247 2.059648195426434 -2.0476856794466016 >50K <=50K Error sum for iteration 800 = 62.35513077654005 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538128609371851 0.1806538768913603 age Hidden Weights: -1.1710490016852213 2.149814321746707 2.174321510141606 -2.1659842503982314 >50K <=50K Error sum for iteration 900 = 62.04594361722027 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538230533361986 0.1956004913654748 age Hidden Weights: -1.2091008570045803 2.188924252391391 2.253732817277411 -2.2477651710822424 >50K <=50K Error sum for iteration 1000 = 61.80960834862369 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538288860908889 0.20759184870832675 age Hidden Weights: -1.2349275549343077 2.2154774911899824 2.307437203288044 -2.30307768098238 >50K <=50K Error sum for iteration 1100 = 61.624539176192506 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538317121337477 0.2174662315983339 age Hidden Weights: -1.2515457867473805 2.2326093056823955 2.342018364802936 -2.33878567080124 >50K <=50K Error sum for iteration 1200 = 61.47992790668731 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538324178167676 0.2257750115428935 age Hidden Weights: -1.261100614346627 2.242535938862261 2.362066295510347 -2.3596439768641133 >50K <=50K Error sum for iteration 1300 = 61.36159955622029 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538316013817666 0.23289436435629293 age Hidden Weights: -1.2651355529143904 2.2468451852593714 2.370840649385934 -2.369013202324474 >50K <=50K Error sum for iteration 1400 = 61.26218257706775 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538296802480198 0.23908967702761158 age Hidden Weights: -1.2647830389901837 2.2466980126506413 2.370708433062693 -2.3693245952563613 >50K <=50K Error sum for iteration 1500 = 61.175196880508175 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538269556040128 0.24455393332525677 age Hidden Weights: -1.260894261085795 2.2429646070234015 2.363433292360967 -2.362384146458864 >50K <=50K Error sum for iteration 1600 = 61.09737616356705 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538236519461415 0.249431374680329 age Hidden Weights: -1.254126722625608 2.2363155044434775 2.3503671325401227 -2.3495725690402 >50K <=50K Error sum for iteration 1700 = 61.02659393059857 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538199418325724 0.2538325307514291 age Hidden Weights: -1.2450033657060364 2.2272828307585453 2.332577834009207 -2.331977901704399 >50K <=50K Error sum for iteration 1800 = 60.962205511417906 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538159617408302 0.25784403149847285 age Hidden Weights: -1.2339527929180736 2.2163018172594344 2.3109352459363715 -2.310484457237654 >50K <=50K Error sum for iteration 1900 = 60.90200200282511 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538118224301197 0.26153517095410217 age Hidden Weights: -1.2213368666601199 2.20373921416923 2.286169711350523 -2.285833189646402 >50K <=50K Error sum for iteration 2000 = 60.84517089324903 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7538076158076493 0.26496239098906815 age Hidden Weights: -1.2074697245433914 2.189912831862069 2.2589121495528297 -2.258662956891336 >50K <=50K Error sum for iteration 2100 = 60.7907882051913 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.753803419503265 0.26817239509735313 age Hidden Weights: -1.192630786194047 2.175104895177781 2.229721385020947 -2.229538624718454 >50K <=50K Error sum for iteration 2200 = 60.73832614860447 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.753799299900727 0.2712043343730046 age Hidden Weights: -1.1770734009297787 2.1595709255033144 2.199102330867435 -2.1989697698666815 >50K <=50K Error sum for iteration 2300 = 60.68747689715909 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537953141065331 0.27409134847535305 age Hidden Weights: -1.1610302182012557 2.143545272242177 2.1675173686544476 -2.167422415158094 >50K <=50K Error sum for iteration 2400 = 60.63795707868188 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537915111833094 0.27686164850856637 age Hidden Weights: -1.1447160329567299 2.1272440707294145 2.1353925296654572 -2.1353254579490812 >50K <=50K Error sum for iteration 2500 = 60.58956477941107 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537879328828837 0.2795392709777045 age Hidden Weights: -1.1283286865639994 2.1108662232553095 2.1031196924472706 -2.1030730427855184 >50K <=50K Error sum for iteration 2600 = 60.54238959985477 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.753784614059852 0.2821445973748359 age Hidden Weights: -1.1120485343184956 2.0945929248015047 2.0710558426466807 -2.0710239479081296 >50K <=50K Error sum for iteration 2700 = 60.496513635010544 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.75378158291164 0.28469471312705863 age Hidden Weights: -1.0960369779565036 2.078586239013044 2.039520398019784 -2.039499004284063 >50K <=50K Error sum for iteration 2800 = 60.45153455397017 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537788611638837 0.28720366629335237 age Hidden Weights: -1.0804345665791042 2.0629872326187946 2.008791602386613 -2.0087775615992984 >50K <=50K Error sum for iteration 2900 = 60.40739037934869 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537764642946871 0.28968267611226434 age Hidden Weights: -1.0653591580036816 2.047914163532341 1.9791029676814402 -1.9790939874995666 >50K <=50K Error sum for iteration 3000 = 60.365635386690805 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537744018625757 0.2921403313785064 age Hidden Weights: -1.0509045802591737 2.0334611644268876 1.9506406423530176 -1.9506350829652308 >50K <=50K Error sum for iteration 3100 = 60.32518192077082 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537726779720074 0.2945828072506052 age Hidden Weights: -1.0371401298505354 2.019697759670156 1.9235423851321523 -1.9235390956704972 >50K <=50K Error sum for iteration 3200 = 60.28584019764647 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537712918801204 0.2970141164927591 age Hidden Weights: -1.0241110971580298 2.0066694066831214 1.897898538083525 -1.8978967268329237 >50K <=50K Error sum for iteration 3300 = 60.24853695804398 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537702387201399 0.2994363985005641 age Hidden Weights: -1.011840342810747 1.9943990859105423 1.8737550651712256 -1.8737541985855501 >50K <=50K Error sum for iteration 3400 = 60.21271170351512 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537695102980808 0.3018502384772717 age Hidden Weights: -1.0003307927829415 1.9828898073253196 1.8511184121490365 -1.851118138078078 >50K <=50K Error sum for iteration 3500 = 60.17809028468649 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537690959098047 0.30425500139322326 age Hidden Weights: -0.9895686026748977 1.9721277840521778 1.829961707018531 -1.8299617978148974 >50K <=50K Error sum for iteration 3600 = 60.1444084306225 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537689831256685 0.3066491617475767 age Hidden Weights: -0.9795266805285892 1.9620859626109612 1.8102316933441231 -1.81023200482384 >50K <=50K Error sum for iteration 3700 = 60.11157740940308 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537691584989504 0.30903061058460957 age Hidden Weights: -0.9701682550238958 1.9527275968276934 1.7918557778547441 -1.7918562204960171 >50K <=50K Error sum for iteration 3800 = 60.08030022130786 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537696081677947 0.3113969248028297 age Hidden Weights: -0.9614502211351171 1.9440095977420193 1.7747486587666998 -1.7747491780419486 >50K <=50K Error sum for iteration 3900 = 60.05026016366303 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.753770318335523 0.3137455891812663 age Hidden Weights: -0.9533260696366369 1.935885466170454 1.758818145781584 -1.7588187090801273 >50K <=50K Error sum for iteration 4000 = 60.02118506456295 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537712756284001 0.3160741673464201 age Hidden Weights: -0.9457482903016631 1.9283076980336047 1.7439699472170884 -1.7439705353697932 >50K <=50K Error sum for iteration 4100 = 59.993053427488334 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537724673395414 0.31838042302106584 age Hidden Weights: -0.9386702151128793 1.9212296290039668 1.7301113517890736 -1.7301119537086445 >50K <=50K Error sum for iteration 4200 = 59.96550193637039 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537738815757797 0.3206623966863952 age Hidden Weights: -0.9320473274313864 1.914606744616443 1.7171538520155258 -1.7171544613840204 >50K <=50K Error sum for iteration 4300 = 59.93852107186694 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537755073261145 0.3229184450505906 age Hidden Weights: -0.9258381022576406 1.9083975211302386 1.7050148353944412 -1.705015448659994 >50K <=50K Error sum for iteration 4400 = 59.91218745460702 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537773344717505 0.32514725158076574 age Hidden Weights: -0.9200044626330947 1.9025638823041788 1.6936185103640626 -1.6936191255537454 >50K <=50K Error sum for iteration 4500 = 59.88646322961347 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537793537552623 0.32734781614122416 age Hidden Weights: -0.914511941775924 1.8970713617592918 1.6828962441336428 -1.6828968601627432 >50K <=50K Error sum for iteration 4600 = 59.86179292846114 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537815567236504 0.32951943086465035 age Hidden Weights: -0.9093296345566136 1.8918890545895417 1.67278647832023 -1.6727870945975365 >50K <=50K Error sum for iteration 4700 = 59.83813978503576 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537839356579394 0.33166164810939225 age Hidden Weights: -0.9044300099421752 1.8869894298844982 1.6632343649863846 -1.6632349811920055 >50K <=50K Error sum for iteration 4800 = 59.81562412337946 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537864834968434 0.3337742449913389 age Hidden Weights: -0.8997886416720561 1.8823480614501642 1.6541912373612142 -1.6541918533230415 >50K <=50K Error sum for iteration 4900 = 59.79396307543564 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537891937613598 0.3358571877011206 age Hidden Weights: -0.8953839001618124 1.8779433197381488 1.6456140012514129 -1.645614616877725 >50K <=50K Options: -I 5000 Back Propagation Neural Network: Numer Input Nodes: 1 Number Hidden Nodes: 2 Number Output Nodes: 2 -I 5000 -E 0.01 -S 1623718248 -H 2 -M 0.01 Input Weights: 0.7537920310605996 0.33789020909243733 age Hidden Weights: -0.8912374894537933 1.8737969088124629 1.6375439998195127 -1.6375446150661213 >50K <=50K === Error on training data === Correctly Classified Instances 387 77.4 % Incorrectly Classified Instances 113 22.6 % Mean absolute error 0.3643 Root mean squared error 0.4064 Relative absolute error 103.9638 % Root relative squared error 97.173 % Total Number of Instances 500 === Confusion Matrix === a b <-- classified as 0 113 | a = >50K 0 387 | b = <=50K === Error on test data === Correctly Classified Instances 385 76.8463 % Incorrectly Classified Instances 116 23.1537 % Mean absolute error 0.3606 Root mean squared error 0.4066 Relative absolute error 102.0278 % Root relative squared error 96.3881 % Total Number of Instances 501 === Confusion Matrix === a b <-- classified as 0 116 | a = >50K 0 385 | b = <=50K