log_5000.txt

<<Load data>> - Successfully loaded clean data

<<Training>> - Matrix Factorisation
Iteration: 0 ; error = 1.1399
TEST MSE - MF MSE: 1.1666460899751736
Iteration: 10 ; error = 0.9118
TEST MSE - MF MSE: 0.9554170821175608
Iteration: 20 ; error = 0.8753
TEST MSE - MF MSE: 0.9093067756869885
Iteration: 30 ; error = 0.8585
TEST MSE - MF MSE: 0.8870474470711124
Iteration: 40 ; error = 0.8479
TEST MSE - MF MSE: 0.8734987711973943
Iteration: 50 ; error = 0.8394
TEST MSE - MF MSE: 0.8637787355228339
Iteration: 60 ; error = 0.8310
TEST MSE - MF MSE: 0.8552559437664469
Iteration: 70 ; error = 0.8209
TEST MSE - MF MSE: 0.8462963018671423
Iteration: 80 ; error = 0.8073
TEST MSE - MF MSE: 0.8353844532474086
Iteration: 90 ; error = 0.7890
TEST MSE - MF MSE: 0.8211232530677817
Iteration: 100 ; error = 0.7658
TEST MSE - MF MSE: 0.8028630977339725
Iteration: 110 ; error = 0.7395
TEST MSE - MF MSE: 0.7809049109760556
Iteration: 120 ; error = 0.7123
TEST MSE - MF MSE: 0.7564061636818084
Iteration: 130 ; error = 0.6851
TEST MSE - MF MSE: 0.7303303014338401
Iteration: 140 ; error = 0.6584
TEST MSE - MF MSE: 0.7034913019758626
Iteration: 150 ; error = 0.6326
TEST MSE - MF MSE: 0.6765795277836361
Iteration: 160 ; error = 0.6081
TEST MSE - MF MSE: 0.6502210981773083
Iteration: 170 ; error = 0.5852
TEST MSE - MF MSE: 0.6250384697934819
Iteration: 180 ; error = 0.5642
TEST MSE - MF MSE: 0.6011582680024093
Iteration: 190 ; error = 0.5451
TEST MSE - MF MSE: 0.5789302650844925
Iteration: 200 ; error = 0.5278
TEST MSE - MF MSE: 0.5582921674529412
Iteration: 210 ; error = 0.5121
TEST MSE - MF MSE: 0.5391574025016153
Iteration: 220 ; error = 0.4981
TEST MSE - MF MSE: 0.5214250556886145
Iteration: 230 ; error = 0.4854
TEST MSE - MF MSE: 0.5051059874678427
Iteration: 240 ; error = 0.4740
TEST MSE - MF MSE: 0.4899862489962478
Iteration: 250 ; error = 0.4636
TEST MSE - MF MSE: 0.4760195740196357
Iteration: 260 ; error = 0.4543
TEST MSE - MF MSE: 0.4631033232890801
Iteration: 270 ; error = 0.4457
TEST MSE - MF MSE: 0.45111534086668786
Iteration: 280 ; error = 0.4380
TEST MSE - MF MSE: 0.4400037935536764
Iteration: 290 ; error = 0.4309
TEST MSE - MF MSE: 0.4297287346159866
Iteration: 300 ; error = 0.4244
TEST MSE - MF MSE: 0.42014330458213
Iteration: 310 ; error = 0.4184
TEST MSE - MF MSE: 0.4112114295864591
Iteration: 320 ; error = 0.4128
TEST MSE - MF MSE: 0.40288101930920517
Iteration: 330 ; error = 0.4077
TEST MSE - MF MSE: 0.39513313643192427
Iteration: 340 ; error = 0.4030
TEST MSE - MF MSE: 0.38785008871629156
Iteration: 350 ; error = 0.3986
TEST MSE - MF MSE: 0.3810662418658964
Iteration: 360 ; error = 0.3944
TEST MSE - MF MSE: 0.37470668830242204
Iteration: 370 ; error = 0.3906
TEST MSE - MF MSE: 0.36875511973306435
Iteration: 380 ; error = 0.3870
TEST MSE - MF MSE: 0.3631189488429788
Iteration: 390 ; error = 0.3836
TEST MSE - MF MSE: 0.35786552264031685
Iteration: 400 ; error = 0.3805
TEST MSE - MF MSE: 0.35291668984012525
Iteration: 410 ; error = 0.3775
TEST MSE - MF MSE: 0.3482673316917098
Iteration: 420 ; error = 0.3747
TEST MSE - MF MSE: 0.34381736026309645
Iteration: 430 ; error = 0.3720
TEST MSE - MF MSE: 0.3396559073995916
Iteration: 440 ; error = 0.3695
TEST MSE - MF MSE: 0.3357313815274277
Iteration: 450 ; error = 0.3671
TEST MSE - MF MSE: 0.33203802973914687
Iteration: 460 ; error = 0.3649
TEST MSE - MF MSE: 0.3285125682861412
Iteration: 470 ; error = 0.3628
TEST MSE - MF MSE: 0.32519126449273866
Iteration: 480 ; error = 0.3608
TEST MSE - MF MSE: 0.32199154172720645
Iteration: 490 ; error = 0.3588
TEST MSE - MF MSE: 0.3190150775613902
Iteration: 500 ; error = 0.3570
TEST MSE - MF MSE: 0.31614951947787373
Iteration: 510 ; error = 0.3553
TEST MSE - MF MSE: 0.3134392588951809
Iteration: 520 ; error = 0.3536
TEST MSE - MF MSE: 0.3108482164394483
Iteration: 530 ; error = 0.3520
TEST MSE - MF MSE: 0.30836227598942745
Iteration: 540 ; error = 0.3505
TEST MSE - MF MSE: 0.3060005547946493
Iteration: 550 ; error = 0.3490
TEST MSE - MF MSE: 0.3037678549354531
Iteration: 560 ; error = 0.3476
TEST MSE - MF MSE: 0.3016155029241674
Iteration: 570 ; error = 0.3463
TEST MSE - MF MSE: 0.29955244489428295
Iteration: 580 ; error = 0.3450
TEST MSE - MF MSE: 0.29755876062760084
Iteration: 590 ; error = 0.3438
TEST MSE - MF MSE: 0.29568641395291245
Iteration: 600 ; error = 0.3426
TEST MSE - MF MSE: 0.2938887590326946
Iteration: 610 ; error = 0.3415
TEST MSE - MF MSE: 0.2921335867753703
Iteration: 620 ; error = 0.3404
TEST MSE - MF MSE: 0.29046014959866456
Iteration: 630 ; error = 0.3393
TEST MSE - MF MSE: 0.2888785336929521
Iteration: 640 ; error = 0.3383
TEST MSE - MF MSE: 0.28733960159708105
Iteration: 650 ; error = 0.3373
TEST MSE - MF MSE: 0.2858500909483282
Iteration: 660 ; error = 0.3364
TEST MSE - MF MSE: 0.28441503926541845
Iteration: 670 ; error = 0.3355
TEST MSE - MF MSE: 0.28304327457693756
Iteration: 680 ; error = 0.3346
TEST MSE - MF MSE: 0.2816885620887682
Iteration: 690 ; error = 0.3337
TEST MSE - MF MSE: 0.28042237512021023
Iteration: 700 ; error = 0.3329
TEST MSE - MF MSE: 0.27915008283567055
Iteration: 710 ; error = 0.3321
TEST MSE - MF MSE: 0.2779770241945514
Iteration: 720 ; error = 0.3313
TEST MSE - MF MSE: 0.2768228466363403
Iteration: 730 ; error = 0.3306
TEST MSE - MF MSE: 0.2756967082127255
Iteration: 740 ; error = 0.3298
TEST MSE - MF MSE: 0.2746035288054527
Iteration: 750 ; error = 0.3291
TEST MSE - MF MSE: 0.27353803936556664
Iteration: 760 ; error = 0.3284
TEST MSE - MF MSE: 0.2725484894447518
Iteration: 770 ; error = 0.3278
TEST MSE - MF MSE: 0.2715511284806541
Iteration: 780 ; error = 0.3271
TEST MSE - MF MSE: 0.2705815272277485
Iteration: 790 ; error = 0.3265
TEST MSE - MF MSE: 0.2696693031878885
Iteration: 800 ; error = 0.3259
TEST MSE - MF MSE: 0.26875079307127403
Iteration: 810 ; error = 0.3253
TEST MSE - MF MSE: 0.2678487040236261
Iteration: 820 ; error = 0.3247
TEST MSE - MF MSE: 0.26702133048792315
Iteration: 830 ; error = 0.3241
TEST MSE - MF MSE: 0.26619190317438257
Iteration: 840 ; error = 0.3236
TEST MSE - MF MSE: 0.26537585780389134
Iteration: 850 ; error = 0.3230
TEST MSE - MF MSE: 0.26457144513930786
Iteration: 860 ; error = 0.3225
TEST MSE - MF MSE: 0.26382056957935374
Iteration: 870 ; error = 0.3220
TEST MSE - MF MSE: 0.26306920747451445
Iteration: 880 ; error = 0.3215
TEST MSE - MF MSE: 0.2623401056557926
Iteration: 890 ; error = 0.3210
TEST MSE - MF MSE: 0.2616330516788756
Iteration: 900 ; error = 0.3206
TEST MSE - MF MSE: 0.2609393612319244
Iteration: 910 ; error = 0.3201
TEST MSE - MF MSE: 0.2602661402375301
Iteration: 920 ; error = 0.3197
TEST MSE - MF MSE: 0.2596131109517176
Iteration: 930 ; error = 0.3192
TEST MSE - MF MSE: 0.25894732097156287
Iteration: 940 ; error = 0.3188
TEST MSE - MF MSE: 0.25833267908132224
Iteration: 950 ; error = 0.3184
TEST MSE - MF MSE: 0.2577083520940869
Iteration: 960 ; error = 0.3180
TEST MSE - MF MSE: 0.25713853554955324
Iteration: 970 ; error = 0.3176
TEST MSE - MF MSE: 0.25652463965497435
Iteration: 980 ; error = 0.3172
TEST MSE - MF MSE: 0.25596513225578993
Iteration: 990 ; error = 0.3168
TEST MSE - MF MSE: 0.25538981831880647
Iteration: 1000 ; error = 0.3164
TEST MSE - MF MSE: 0.2548525477178267
Iteration: 1010 ; error = 0.3161
TEST MSE - MF MSE: 0.2543082758119205
Iteration: 1020 ; error = 0.3157
TEST MSE - MF MSE: 0.25376294627794377
Iteration: 1030 ; error = 0.3154
TEST MSE - MF MSE: 0.2532651277508619
Iteration: 1040 ; error = 0.3150
TEST MSE - MF MSE: 0.2527337182162714
Iteration: 1050 ; error = 0.3147
TEST MSE - MF MSE: 0.2522665313310682
Iteration: 1060 ; error = 0.3144
TEST MSE - MF MSE: 0.2517663030202876
Iteration: 1070 ; error = 0.3140
TEST MSE - MF MSE: 0.25128529887299866
Iteration: 1080 ; error = 0.3137
TEST MSE - MF MSE: 0.25082564888404046
Iteration: 1090 ; error = 0.3134
TEST MSE - MF MSE: 0.25037529417517934
Iteration: 1100 ; error = 0.3131
TEST MSE - MF MSE: 0.2499286798942622
Iteration: 1110 ; error = 0.3128
TEST MSE - MF MSE: 0.24949719815620502
Iteration: 1120 ; error = 0.3125
TEST MSE - MF MSE: 0.24905073354567878
Iteration: 1130 ; error = 0.3123
TEST MSE - MF MSE: 0.24866753935476077
Iteration: 1140 ; error = 0.3120
TEST MSE - MF MSE: 0.24823827194074236
Iteration: 1150 ; error = 0.3117
TEST MSE - MF MSE: 0.2478143879218211
Iteration: 1160 ; error = 0.3114
TEST MSE - MF MSE: 0.24739869932550257
Iteration: 1170 ; error = 0.3112
TEST MSE - MF MSE: 0.24702596285324546
Iteration: 1180 ; error = 0.3109
TEST MSE - MF MSE: 0.2466392743697247
Iteration: 1190 ; error = 0.3107
TEST MSE - MF MSE: 0.24624841046180573
Iteration: 1200 ; error = 0.3104
TEST MSE - MF MSE: 0.24590355203311967
Iteration: 1210 ; error = 0.3102
TEST MSE - MF MSE: 0.2455325746645868
Iteration: 1220 ; error = 0.3099
TEST MSE - MF MSE: 0.2451610697262667
Iteration: 1230 ; error = 0.3097
TEST MSE - MF MSE: 0.24480979277850407
Iteration: 1240 ; error = 0.3095
TEST MSE - MF MSE: 0.2444767269172592
Iteration: 1250 ; error = 0.3092
TEST MSE - MF MSE: 0.24413063598092777
Iteration: 1260 ; error = 0.3090
TEST MSE - MF MSE: 0.24377797358420394
Iteration: 1270 ; error = 0.3088
TEST MSE - MF MSE: 0.2434654403406537
Iteration: 1280 ; error = 0.3086
TEST MSE - MF MSE: 0.2431327917507432
Iteration: 1290 ; error = 0.3083
TEST MSE - MF MSE: 0.2428159113303897
Iteration: 1300 ; error = 0.3081
TEST MSE - MF MSE: 0.24251427138556214
Iteration: 1310 ; error = 0.3079
TEST MSE - MF MSE: 0.24219635940499118
Iteration: 1320 ; error = 0.3077
TEST MSE - MF MSE: 0.24186600244634615
Iteration: 1330 ; error = 0.3075
TEST MSE - MF MSE: 0.2415793051433646
Iteration: 1340 ; error = 0.3073
TEST MSE - MF MSE: 0.24128291698393367
Iteration: 1350 ; error = 0.3071
TEST MSE - MF MSE: 0.2410003125156626
Iteration: 1360 ; error = 0.3070
TEST MSE - MF MSE: 0.2407185095657519
Iteration: 1370 ; error = 0.3068
TEST MSE - MF MSE: 0.2404396230752311
Iteration: 1380 ; error = 0.3066
TEST MSE - MF MSE: 0.2401480399339501
Iteration: 1390 ; error = 0.3064
TEST MSE - MF MSE: 0.23988068742913873
Iteration: 1400 ; error = 0.3062
TEST MSE - MF MSE: 0.23961564592708448
Iteration: 1410 ; error = 0.3061
TEST MSE - MF MSE: 0.2393203524076168
Iteration: 1420 ; error = 0.3059
TEST MSE - MF MSE: 0.239106358820693
Iteration: 1430 ; error = 0.3057
TEST MSE - MF MSE: 0.23882329616573103
Iteration: 1440 ; error = 0.3055
TEST MSE - MF MSE: 0.23858839445868738
Iteration: 1450 ; error = 0.3054
TEST MSE - MF MSE: 0.23834102279034294
Iteration: 1460 ; error = 0.3052
TEST MSE - MF MSE: 0.23809157742679815
Iteration: 1470 ; error = 0.3051
TEST MSE - MF MSE: 0.2378469388803546
Iteration: 1480 ; error = 0.3049
TEST MSE - MF MSE: 0.2376067886039554
Iteration: 1490 ; error = 0.3047
TEST MSE - MF MSE: 0.23736452273912884
Iteration: 1500 ; error = 0.3046
TEST MSE - MF MSE: 0.23715183228763073
Iteration: 1510 ; error = 0.3044
TEST MSE - MF MSE: 0.23689900099036823
Iteration: 1520 ; error = 0.3043
TEST MSE - MF MSE: 0.23668291954663864
Iteration: 1530 ; error = 0.3041
TEST MSE - MF MSE: 0.23646735317728057
Iteration: 1540 ; error = 0.3040
TEST MSE - MF MSE: 0.2362456237787951
Iteration: 1550 ; error = 0.3038
TEST MSE - MF MSE: 0.23601630093338194
Iteration: 1560 ; error = 0.3037
TEST MSE - MF MSE: 0.23582567935709925
Iteration: 1570 ; error = 0.3036
TEST MSE - MF MSE: 0.2355913836737635
Iteration: 1580 ; error = 0.3034
TEST MSE - MF MSE: 0.23542947926202512
Iteration: 1590 ; error = 0.3033
TEST MSE - MF MSE: 0.23520647253241528
Iteration: 1600 ; error = 0.3032
TEST MSE - MF MSE: 0.23499335912153294
Iteration: 1610 ; error = 0.3030
TEST MSE - MF MSE: 0.2347891320022888
Iteration: 1620 ; error = 0.3029
TEST MSE - MF MSE: 0.23460155682035863
Iteration: 1630 ; error = 0.3028
TEST MSE - MF MSE: 0.23439464672192512
Iteration: 1640 ; error = 0.3026
TEST MSE - MF MSE: 0.23421008140952376
Iteration: 1650 ; error = 0.3025
TEST MSE - MF MSE: 0.23403511905901123
Iteration: 1660 ; error = 0.3024
TEST MSE - MF MSE: 0.2338087290643314
Iteration: 1670 ; error = 0.3023
TEST MSE - MF MSE: 0.23364582404062695
Iteration: 1680 ; error = 0.3021
TEST MSE - MF MSE: 0.23345399194456853
Iteration: 1690 ; error = 0.3020
TEST MSE - MF MSE: 0.2332742737184257
Iteration: 1700 ; error = 0.3019
TEST MSE - MF MSE: 0.23310804086227355
Iteration: 1710 ; error = 0.3018
TEST MSE - MF MSE: 0.232918837500609
Iteration: 1720 ; error = 0.3017
TEST MSE - MF MSE: 0.23275243112778332
Iteration: 1730 ; error = 0.3016
TEST MSE - MF MSE: 0.2325778369730882
Iteration: 1740 ; error = 0.3014
TEST MSE - MF MSE: 0.2324157465356919
Iteration: 1750 ; error = 0.3013
TEST MSE - MF MSE: 0.23224869903542356
Iteration: 1760 ; error = 0.3012
TEST MSE - MF MSE: 0.2320938427068426
Iteration: 1770 ; error = 0.3011
TEST MSE - MF MSE: 0.23191879509032523
Iteration: 1780 ; error = 0.3010
TEST MSE - MF MSE: 0.23176877052517822
Iteration: 1790 ; error = 0.3009
TEST MSE - MF MSE: 0.2315936752375539
Iteration: 1800 ; error = 0.3008
TEST MSE - MF MSE: 0.23144748322501046
Iteration: 1810 ; error = 0.3007
TEST MSE - MF MSE: 0.23129080634273222
Iteration: 1820 ; error = 0.3006
TEST MSE - MF MSE: 0.2311464142196881
Iteration: 1830 ; error = 0.3005
TEST MSE - MF MSE: 0.23098122019124495
Iteration: 1840 ; error = 0.3004
TEST MSE - MF MSE: 0.2308308242834073
Iteration: 1850 ; error = 0.3003
TEST MSE - MF MSE: 0.23067211196757226
Iteration: 1860 ; error = 0.3002
TEST MSE - MF MSE: 0.2305439947305848
Iteration: 1870 ; error = 0.3001
TEST MSE - MF MSE: 0.23038713498641036
Iteration: 1880 ; error = 0.3000
TEST MSE - MF MSE: 0.23024809660710466
Iteration: 1890 ; error = 0.2999
TEST MSE - MF MSE: 0.230096197624272
Iteration: 1900 ; error = 0.2998
TEST MSE - MF MSE: 0.2299626181442741
Iteration: 1910 ; error = 0.2997
TEST MSE - MF MSE: 0.22983519367463512
Iteration: 1920 ; error = 0.2996
TEST MSE - MF MSE: 0.22968191437229582
Iteration: 1930 ; error = 0.2995
TEST MSE - MF MSE: 0.22955765641798584
Iteration: 1940 ; error = 0.2994
TEST MSE - MF MSE: 0.229419851652785
Iteration: 1950 ; error = 0.2994
TEST MSE - MF MSE: 0.22928292278055068
Iteration: 1960 ; error = 0.2993
TEST MSE - MF MSE: 0.22916207398784405
Iteration: 1970 ; error = 0.2992
TEST MSE - MF MSE: 0.22902154942133884
Iteration: 1980 ; error = 0.2991
TEST MSE - MF MSE: 0.2289023968180479
Iteration: 1990 ; error = 0.2990
TEST MSE - MF MSE: 0.22876777857979802
Iteration: 2000 ; error = 0.2989
TEST MSE - MF MSE: 0.2286413073569745
Iteration: 2010 ; error = 0.2988
TEST MSE - MF MSE: 0.22851146718048806
Iteration: 2020 ; error = 0.2988
TEST MSE - MF MSE: 0.22841416885342553
Iteration: 2030 ; error = 0.2987
TEST MSE - MF MSE: 0.2282767969625383
Iteration: 2040 ; error = 0.2986
TEST MSE - MF MSE: 0.22815351561966485
Iteration: 2050 ; error = 0.2985
TEST MSE - MF MSE: 0.22804334684881947
Iteration: 2060 ; error = 0.2984
TEST MSE - MF MSE: 0.22792030710086383
Iteration: 2070 ; error = 0.2984
TEST MSE - MF MSE: 0.227794450094546
Iteration: 2080 ; error = 0.2983
TEST MSE - MF MSE: 0.22768757468595371
Iteration: 2090 ; error = 0.2982
TEST MSE - MF MSE: 0.22755261737697663
Iteration: 2100 ; error = 0.2981
TEST MSE - MF MSE: 0.2274499604608241
Iteration: 2110 ; error = 0.2981
TEST MSE - MF MSE: 0.22733303484711234
Iteration: 2120 ; error = 0.2980
TEST MSE - MF MSE: 0.22723566266488662
Iteration: 2130 ; error = 0.2979
TEST MSE - MF MSE: 0.22711915711744993
Iteration: 2140 ; error = 0.2978
TEST MSE - MF MSE: 0.2270288116748514
Iteration: 2150 ; error = 0.2978
TEST MSE - MF MSE: 0.22690769163973215
Iteration: 2160 ; error = 0.2977
TEST MSE - MF MSE: 0.2268050001669138
Iteration: 2170 ; error = 0.2976
TEST MSE - MF MSE: 0.2267086422163206
Iteration: 2180 ; error = 0.2975
TEST MSE - MF MSE: 0.22658642066994295
Iteration: 2190 ; error = 0.2975
TEST MSE - MF MSE: 0.22647503091697924
Iteration: 2200 ; error = 0.2974
TEST MSE - MF MSE: 0.22637759088543072
Iteration: 2210 ; error = 0.2973
TEST MSE - MF MSE: 0.2262803373433226
Iteration: 2220 ; error = 0.2973
TEST MSE - MF MSE: 0.2261723349887172
Iteration: 2230 ; error = 0.2972
TEST MSE - MF MSE: 0.22608521163677947
Iteration: 2240 ; error = 0.2971
TEST MSE - MF MSE: 0.22599532204151315
Iteration: 2250 ; error = 0.2971
TEST MSE - MF MSE: 0.22589598096460964
Iteration: 2260 ; error = 0.2970
TEST MSE - MF MSE: 0.225787462558227
Iteration: 2270 ; error = 0.2969
TEST MSE - MF MSE: 0.2257059448334076
Iteration: 2280 ; error = 0.2969
TEST MSE - MF MSE: 0.22561877764029692
Iteration: 2290 ; error = 0.2968
TEST MSE - MF MSE: 0.22551869888851747
Iteration: 2300 ; error = 0.2967
TEST MSE - MF MSE: 0.22543732162617827
Iteration: 2310 ; error = 0.2967
TEST MSE - MF MSE: 0.22534227555271633
Iteration: 2320 ; error = 0.2966
TEST MSE - MF MSE: 0.22523017823950034
Iteration: 2330 ; error = 0.2966
TEST MSE - MF MSE: 0.22515002618536364
Iteration: 2340 ; error = 0.2965
TEST MSE - MF MSE: 0.22505920210988062
Iteration: 2350 ; error = 0.2964
TEST MSE - MF MSE: 0.2249434505809617
Iteration: 2360 ; error = 0.2964
TEST MSE - MF MSE: 0.2248736937563653
Iteration: 2370 ; error = 0.2963
TEST MSE - MF MSE: 0.22477745944149602
Iteration: 2380 ; error = 0.2963
TEST MSE - MF MSE: 0.2247129404686977
Iteration: 2390 ; error = 0.2962
TEST MSE - MF MSE: 0.22461433497323596
Iteration: 2400 ; error = 0.2961
TEST MSE - MF MSE: 0.22453962698965135
Iteration: 2410 ; error = 0.2961
TEST MSE - MF MSE: 0.2244638621200122
Iteration: 2420 ; error = 0.2960
TEST MSE - MF MSE: 0.22437439497470751
Iteration: 2430 ; error = 0.2960
TEST MSE - MF MSE: 0.22429441687384266
Iteration: 2440 ; error = 0.2959
TEST MSE - MF MSE: 0.22422529827971566
Iteration: 2450 ; error = 0.2959
TEST MSE - MF MSE: 0.2241236077181172
Iteration: 2460 ; error = 0.2958
TEST MSE - MF MSE: 0.2240473493600765
Iteration: 2470 ; error = 0.2957
TEST MSE - MF MSE: 0.22395543916990351
Iteration: 2480 ; error = 0.2957
TEST MSE - MF MSE: 0.2238856172892006
Iteration: 2490 ; error = 0.2956
TEST MSE - MF MSE: 0.22381141695297938
Iteration: 2500 ; error = 0.2956
TEST MSE - MF MSE: 0.22372476766467678
Iteration: 2510 ; error = 0.2955
TEST MSE - MF MSE: 0.22366184635025185
Iteration: 2520 ; error = 0.2955
TEST MSE - MF MSE: 0.22357893948419832
Iteration: 2530 ; error = 0.2954
TEST MSE - MF MSE: 0.2234777182106874
Iteration: 2540 ; error = 0.2954
TEST MSE - MF MSE: 0.22341628604043964
Iteration: 2550 ; error = 0.2953
TEST MSE - MF MSE: 0.22333952957124215
Iteration: 2560 ; error = 0.2953
TEST MSE - MF MSE: 0.22329409052105378
Iteration: 2570 ; error = 0.2952
TEST MSE - MF MSE: 0.22321650057125988
Iteration: 2580 ; error = 0.2952
TEST MSE - MF MSE: 0.2231342284501029
Iteration: 2590 ; error = 0.2951
TEST MSE - MF MSE: 0.2230761179761503
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TEST MSE - MF MSE: 0.2229830137727432
Iteration: 2610 ; error = 0.2950
TEST MSE - MF MSE: 0.22294310763848652
Iteration: 2620 ; error = 0.2950
TEST MSE - MF MSE: 0.22286983679735392
Iteration: 2630 ; error = 0.2949
TEST MSE - MF MSE: 0.22276175091988537
Iteration: 2640 ; error = 0.2949
TEST MSE - MF MSE: 0.22272115639753298
Iteration: 2650 ; error = 0.2948
TEST MSE - MF MSE: 0.22262629801100958
Iteration: 2660 ; error = 0.2948
TEST MSE - MF MSE: 0.22256873687958423
Iteration: 2670 ; error = 0.2947
TEST MSE - MF MSE: 0.22251645162117595
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TEST MSE - MF MSE: 0.22244255869266755
Iteration: 2690 ; error = 0.2946
TEST MSE - MF MSE: 0.22238255335919604
Iteration: 2700 ; error = 0.2946
TEST MSE - MF MSE: 0.22230736101669113
Iteration: 2710 ; error = 0.2945
TEST MSE - MF MSE: 0.22226581804303822
Iteration: 2720 ; error = 0.2945
TEST MSE - MF MSE: 0.22217457002457633
Iteration: 2730 ; error = 0.2944
TEST MSE - MF MSE: 0.222114215840205
Iteration: 2740 ; error = 0.2944
TEST MSE - MF MSE: 0.22204461990774085
Iteration: 2750 ; error = 0.2944
TEST MSE - MF MSE: 0.22200446113992603
Iteration: 2760 ; error = 0.2943
TEST MSE - MF MSE: 0.22194545498126753
Iteration: 2770 ; error = 0.2943
TEST MSE - MF MSE: 0.2218721344350792
Iteration: 2780 ; error = 0.2942
TEST MSE - MF MSE: 0.22182305469778546
Iteration: 2790 ; error = 0.2942
TEST MSE - MF MSE: 0.22174165745869046
Iteration: 2800 ; error = 0.2941
TEST MSE - MF MSE: 0.22168978527738054
Iteration: 2810 ; error = 0.2941
TEST MSE - MF MSE: 0.2216378389608123
Iteration: 2820 ; error = 0.2940
TEST MSE - MF MSE: 0.22157647729194435
Iteration: 2830 ; error = 0.2940
TEST MSE - MF MSE: 0.22153285113184004
Iteration: 2840 ; error = 0.2940
TEST MSE - MF MSE: 0.22144937037960613
Iteration: 2850 ; error = 0.2939
TEST MSE - MF MSE: 0.22140204876722813
Iteration: 2860 ; error = 0.2939
TEST MSE - MF MSE: 0.2213386687897399
Iteration: 2870 ; error = 0.2938
TEST MSE - MF MSE: 0.22128342701880277
Iteration: 2880 ; error = 0.2938
TEST MSE - MF MSE: 0.22124194820707718
Iteration: 2890 ; error = 0.2938
TEST MSE - MF MSE: 0.2211635222154833
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TEST MSE - MF MSE: 0.2210963833116287
Iteration: 2910 ; error = 0.2937
TEST MSE - MF MSE: 0.22103710741904542
Iteration: 2920 ; error = 0.2936
TEST MSE - MF MSE: 0.22100582179114928
Iteration: 2930 ; error = 0.2936
TEST MSE - MF MSE: 0.2209115779669719
Iteration: 2940 ; error = 0.2936
TEST MSE - MF MSE: 0.22087274701012696
Iteration: 2950 ; error = 0.2935
TEST MSE - MF MSE: 0.22083336045862298
Iteration: 2960 ; error = 0.2935
TEST MSE - MF MSE: 0.22079259518568112
Iteration: 2970 ; error = 0.2934
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Iteration: 2980 ; error = 0.2934
TEST MSE - MF MSE: 0.22068445183169455
Iteration: 2990 ; error = 0.2934
TEST MSE - MF MSE: 0.22064416962025327
Iteration: 3000 ; error = 0.2933
TEST MSE - MF MSE: 0.2205731531202124
Iteration: 3010 ; error = 0.2933
TEST MSE - MF MSE: 0.2205230285646909
Iteration: 3020 ; error = 0.2933
TEST MSE - MF MSE: 0.22049146893465152
Iteration: 3030 ; error = 0.2932
TEST MSE - MF MSE: 0.22041480048952916
Iteration: 3040 ; error = 0.2932
TEST MSE - MF MSE: 0.22037808302866135
Iteration: 3050 ; error = 0.2931
TEST MSE - MF MSE: 0.22033083766625824
Iteration: 3060 ; error = 0.2931
TEST MSE - MF MSE: 0.22029290149031794
Iteration: 3070 ; error = 0.2931
TEST MSE - MF MSE: 0.22022404333357
Iteration: 3080 ; error = 0.2930
TEST MSE - MF MSE: 0.22019235474843113
Iteration: 3090 ; error = 0.2930
TEST MSE - MF MSE: 0.22013895921371882
Iteration: 3100 ; error = 0.2930
TEST MSE - MF MSE: 0.22010064183178898
Iteration: 3110 ; error = 0.2929
TEST MSE - MF MSE: 0.22002957476556928
Iteration: 3120 ; error = 0.2929
TEST MSE - MF MSE: 0.2199944434684352
Iteration: 3130 ; error = 0.2929
TEST MSE - MF MSE: 0.21995156750249928
Iteration: 3140 ; error = 0.2928
TEST MSE - MF MSE: 0.21990343202574628
Iteration: 3150 ; error = 0.2928
TEST MSE - MF MSE: 0.2198444909087326
Iteration: 3160 ; error = 0.2927
TEST MSE - MF MSE: 0.21978057104692933
Iteration: 3170 ; error = 0.2927
TEST MSE - MF MSE: 0.2197571430911223
Iteration: 3180 ; error = 0.2927
TEST MSE - MF MSE: 0.21971882318856156
Iteration: 3190 ; error = 0.2926
TEST MSE - MF MSE: 0.2196672831064034
Iteration: 3200 ; error = 0.2926
TEST MSE - MF MSE: 0.21960656590523311
Iteration: 3210 ; error = 0.2926
TEST MSE - MF MSE: 0.2195584779576549
Iteration: 3220 ; error = 0.2925
TEST MSE - MF MSE: 0.21954235260975222
Iteration: 3230 ; error = 0.2925
TEST MSE - MF MSE: 0.21948792680402646
Iteration: 3240 ; error = 0.2925
TEST MSE - MF MSE: 0.21945691302043394
Iteration: 3250 ; error = 0.2925
TEST MSE - MF MSE: 0.21940582144440268
Iteration: 3260 ; error = 0.2924
TEST MSE - MF MSE: 0.21935694126912134
Iteration: 3270 ; error = 0.2924
TEST MSE - MF MSE: 0.21929554823772796
Iteration: 3280 ; error = 0.2923
TEST MSE - MF MSE: 0.219259114712477
Iteration: 3290 ; error = 0.2923
TEST MSE - MF MSE: 0.2192251175844276
Iteration: 3300 ; error = 0.2923
TEST MSE - MF MSE: 0.21918793662266123
Iteration: 3310 ; error = 0.2923
TEST MSE - MF MSE: 0.21914151111629154
Iteration: 3320 ; error = 0.2922
TEST MSE - MF MSE: 0.21911778244063584
Iteration: 3330 ; error = 0.2922
TEST MSE - MF MSE: 0.21906489676298174
Iteration: 3340 ; error = 0.2922
TEST MSE - MF MSE: 0.21901898848556844
Iteration: 3350 ; error = 0.2921
TEST MSE - MF MSE: 0.2189750946800591
Iteration: 3360 ; error = 0.2921
TEST MSE - MF MSE: 0.21890927480189842
Iteration: 3370 ; error = 0.2921
TEST MSE - MF MSE: 0.21889374196554826
Iteration: 3380 ; error = 0.2920
TEST MSE - MF MSE: 0.21885121913939934
Iteration: 3390 ; error = 0.2920
TEST MSE - MF MSE: 0.21881009944564248
Iteration: 3400 ; error = 0.2920
TEST MSE - MF MSE: 0.2187796959359675
Iteration: 3410 ; error = 0.2919
TEST MSE - MF MSE: 0.21873265275669745
Iteration: 3420 ; error = 0.2919
TEST MSE - MF MSE: 0.21868081706953096
Iteration: 3430 ; error = 0.2919
TEST MSE - MF MSE: 0.21867488590046494
Iteration: 3440 ; error = 0.2919
TEST MSE - MF MSE: 0.2185989004980426
Iteration: 3450 ; error = 0.2918
TEST MSE - MF MSE: 0.21856536942684676
Iteration: 3460 ; error = 0.2918
TEST MSE - MF MSE: 0.21854285328455014
Iteration: 3470 ; error = 0.2918
TEST MSE - MF MSE: 0.21850018149677766
Iteration: 3480 ; error = 0.2917
TEST MSE - MF MSE: 0.21847443692066057
Iteration: 3490 ; error = 0.2917
TEST MSE - MF MSE: 0.21842470134229028
Iteration: 3500 ; error = 0.2917
TEST MSE - MF MSE: 0.2183852873926576
Iteration: 3510 ; error = 0.2917
TEST MSE - MF MSE: 0.2183572921189198
Iteration: 3520 ; error = 0.2916
TEST MSE - MF MSE: 0.21833565068821026
Iteration: 3530 ; error = 0.2916
TEST MSE - MF MSE: 0.21829607149932936
Iteration: 3540 ; error = 0.2916
TEST MSE - MF MSE: 0.21827893354282174
Iteration: 3550 ; error = 0.2915
TEST MSE - MF MSE: 0.21821941134283268
Iteration: 3560 ; error = 0.2915
TEST MSE - MF MSE: 0.21819872401991441
Iteration: 3570 ; error = 0.2915
TEST MSE - MF MSE: 0.2181674185040848
Iteration: 3580 ; error = 0.2915
TEST MSE - MF MSE: 0.21813539053639297
Iteration: 3590 ; error = 0.2914
TEST MSE - MF MSE: 0.21810196421129063
Iteration: 3600 ; error = 0.2914
TEST MSE - MF MSE: 0.2180393517185229
Iteration: 3610 ; error = 0.2914
TEST MSE - MF MSE: 0.21799871433849083
Iteration: 3620 ; error = 0.2914
TEST MSE - MF MSE: 0.21797901827340596
Iteration: 3630 ; error = 0.2913
TEST MSE - MF MSE: 0.21794253739569738
Iteration: 3640 ; error = 0.2913
TEST MSE - MF MSE: 0.217920405630856
Iteration: 3650 ; error = 0.2913
TEST MSE - MF MSE: 0.21788310434880878
Iteration: 3660 ; error = 0.2913
TEST MSE - MF MSE: 0.21787066150925036
Iteration: 3670 ; error = 0.2912
TEST MSE - MF MSE: 0.21781379453610766
Iteration: 3680 ; error = 0.2912
TEST MSE - MF MSE: 0.21776774555952097
Iteration: 3690 ; error = 0.2912
TEST MSE - MF MSE: 0.2177504436658998
Iteration: 3700 ; error = 0.2911
TEST MSE - MF MSE: 0.21771888547750026
Iteration: 3710 ; error = 0.2911
TEST MSE - MF MSE: 0.21768576134325873
Iteration: 3720 ; error = 0.2911
TEST MSE - MF MSE: 0.2176653256374564
Iteration: 3730 ; error = 0.2911
TEST MSE - MF MSE: 0.21762695966696008
Iteration: 3740 ; error = 0.2911
TEST MSE - MF MSE: 0.2175788443883051
Iteration: 3750 ; error = 0.2910
TEST MSE - MF MSE: 0.21757666462244488
Iteration: 3760 ; error = 0.2910
TEST MSE - MF MSE: 0.21753034875792784
Iteration: 3770 ; error = 0.2910
TEST MSE - MF MSE: 0.21747345518425498
Iteration: 3780 ; error = 0.2909
TEST MSE - MF MSE: 0.21747579301038283
Iteration: 3790 ; error = 0.2909
TEST MSE - MF MSE: 0.21742130291687328
Iteration: 3800 ; error = 0.2909
TEST MSE - MF MSE: 0.21739389272027568
Iteration: 3810 ; error = 0.2909
TEST MSE - MF MSE: 0.21735427577022925
Iteration: 3820 ; error = 0.2909
TEST MSE - MF MSE: 0.21734256866004856
Iteration: 3830 ; error = 0.2908
TEST MSE - MF MSE: 0.21732492614768695
Iteration: 3840 ; error = 0.2908
TEST MSE - MF MSE: 0.21727840781421057
Iteration: 3850 ; error = 0.2908
TEST MSE - MF MSE: 0.21723544452114402
Iteration: 3860 ; error = 0.2908
TEST MSE - MF MSE: 0.21720976246714815
Iteration: 3870 ; error = 0.2907
TEST MSE - MF MSE: 0.2171536708258129
Iteration: 3880 ; error = 0.2907
TEST MSE - MF MSE: 0.2171719641764717
Iteration: 3890 ; error = 0.2907
TEST MSE - MF MSE: 0.21711366482802869
Iteration: 3900 ; error = 0.2907
TEST MSE - MF MSE: 0.217071795468572
Iteration: 3910 ; error = 0.2906
TEST MSE - MF MSE: 0.21707268481740263
Iteration: 3920 ; error = 0.2906
TEST MSE - MF MSE: 0.2170349757626319
Iteration: 3930 ; error = 0.2906
TEST MSE - MF MSE: 0.21700497141297564
Iteration: 3940 ; error = 0.2906
TEST MSE - MF MSE: 0.21696863968590593
Iteration: 3950 ; error = 0.2905
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Iteration: 3960 ; error = 0.2905
TEST MSE - MF MSE: 0.2169483676831097
Iteration: 3970 ; error = 0.2905
TEST MSE - MF MSE: 0.2169053414694722
Iteration: 3980 ; error = 0.2905
TEST MSE - MF MSE: 0.21687453005404295
Iteration: 3990 ; error = 0.2905
TEST MSE - MF MSE: 0.21683543157428203
Iteration: 4000 ; error = 0.2904
TEST MSE - MF MSE: 0.21681918780349657
Iteration: 4010 ; error = 0.2904
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Iteration: 4020 ; error = 0.2904
TEST MSE - MF MSE: 0.21676043919348317
Iteration: 4030 ; error = 0.2904
TEST MSE - MF MSE: 0.21673172271377283
Iteration: 4040 ; error = 0.2903
TEST MSE - MF MSE: 0.21670960525039795
Iteration: 4050 ; error = 0.2903
TEST MSE - MF MSE: 0.2166808643952524
Iteration: 4060 ; error = 0.2903
TEST MSE - MF MSE: 0.21665643831105444
Iteration: 4070 ; error = 0.2903
TEST MSE - MF MSE: 0.21662932007196714
Iteration: 4080 ; error = 0.2903
TEST MSE - MF MSE: 0.2165966164089399
Iteration: 4090 ; error = 0.2902
TEST MSE - MF MSE: 0.21655985832017657
Iteration: 4100 ; error = 0.2902
TEST MSE - MF MSE: 0.21654624620858406
Iteration: 4110 ; error = 0.2902
TEST MSE - MF MSE: 0.2165265845412637
Iteration: 4120 ; error = 0.2902
TEST MSE - MF MSE: 0.2164860673001083
Iteration: 4130 ; error = 0.2902
TEST MSE - MF MSE: 0.21643714037371897
Iteration: 4140 ; error = 0.2901
TEST MSE - MF MSE: 0.21644995247013785
Iteration: 4150 ; error = 0.2901
TEST MSE - MF MSE: 0.21641513052665992
Iteration: 4160 ; error = 0.2901
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Iteration: 4170 ; error = 0.2901
TEST MSE - MF MSE: 0.2163666528205655
Iteration: 4180 ; error = 0.2900
TEST MSE - MF MSE: 0.21634681505733575
Iteration: 4190 ; error = 0.2900
TEST MSE - MF MSE: 0.21632149153667307
Iteration: 4200 ; error = 0.2900
TEST MSE - MF MSE: 0.21631734010207257
Iteration: 4210 ; error = 0.2900
TEST MSE - MF MSE: 0.21627776922425818
Iteration: 4220 ; error = 0.2900
TEST MSE - MF MSE: 0.21624544360965142
Iteration: 4230 ; error = 0.2899
TEST MSE - MF MSE: 0.21620682372020703
Iteration: 4240 ; error = 0.2899
TEST MSE - MF MSE: 0.21620818623448945
Iteration: 4250 ; error = 0.2899
TEST MSE - MF MSE: 0.21617429644867892
Iteration: 4260 ; error = 0.2899
TEST MSE - MF MSE: 0.21615695934734322
Iteration: 4270 ; error = 0.2899
TEST MSE - MF MSE: 0.2161336228051749
Iteration: 4280 ; error = 0.2898
TEST MSE - MF MSE: 0.21611969131373815
Iteration: 4290 ; error = 0.2898
TEST MSE - MF MSE: 0.21606290453315918
Iteration: 4300 ; error = 0.2898
TEST MSE - MF MSE: 0.2160671656781791
Iteration: 4310 ; error = 0.2898
TEST MSE - MF MSE: 0.21602144372521617
Iteration: 4320 ; error = 0.2898
TEST MSE - MF MSE: 0.2160042686452126
Iteration: 4330 ; error = 0.2897
TEST MSE - MF MSE: 0.21599371448841304
Iteration: 4340 ; error = 0.2897
TEST MSE - MF MSE: 0.21597323910225352
Iteration: 4350 ; error = 0.2897
TEST MSE - MF MSE: 0.21594915408057203
Iteration: 4360 ; error = 0.2897
TEST MSE - MF MSE: 0.2159230507667983
Iteration: 4370 ; error = 0.2897
TEST MSE - MF MSE: 0.21590825555982002
Iteration: 4380 ; error = 0.2897
TEST MSE - MF MSE: 0.21586355266961726
Iteration: 4390 ; error = 0.2896
TEST MSE - MF MSE: 0.21586370042193426
Iteration: 4400 ; error = 0.2896
TEST MSE - MF MSE: 0.21585473957551138
Iteration: 4410 ; error = 0.2896
TEST MSE - MF MSE: 0.21583015336219652
Iteration: 4420 ; error = 0.2896
TEST MSE - MF MSE: 0.21579213168218814
Iteration: 4430 ; error = 0.2896
TEST MSE - MF MSE: 0.21577533198186594
Iteration: 4440 ; error = 0.2895
TEST MSE - MF MSE: 0.21574203935219888
Iteration: 4450 ; error = 0.2895
TEST MSE - MF MSE: 0.2157261867992008
Iteration: 4460 ; error = 0.2895
TEST MSE - MF MSE: 0.21570912012855167
Iteration: 4470 ; error = 0.2895
TEST MSE - MF MSE: 0.21568566001822878
Iteration: 4480 ; error = 0.2895
TEST MSE - MF MSE: 0.21565080152044175
Iteration: 4490 ; error = 0.2894
TEST MSE - MF MSE: 0.21562563926442485
Iteration: 4500 ; error = 0.2894
TEST MSE - MF MSE: 0.21561329913324492
Iteration: 4510 ; error = 0.2894
TEST MSE - MF MSE: 0.2156198596063406
Iteration: 4520 ; error = 0.2894
TEST MSE - MF MSE: 0.21555149866886458
Iteration: 4530 ; error = 0.2894
TEST MSE - MF MSE: 0.2155519422629139
Iteration: 4540 ; error = 0.2894
TEST MSE - MF MSE: 0.2155226455740193
Iteration: 4550 ; error = 0.2893
TEST MSE - MF MSE: 0.2155081201116532
Iteration: 4560 ; error = 0.2893
TEST MSE - MF MSE: 0.21547694194693925
Iteration: 4570 ; error = 0.2893
TEST MSE - MF MSE: 0.21546960558841477
Iteration: 4580 ; error = 0.2893
TEST MSE - MF MSE: 0.21546200751700928
Iteration: 4590 ; error = 0.2893
TEST MSE - MF MSE: 0.21542043269314737
Iteration: 4600 ; error = 0.2893
TEST MSE - MF MSE: 0.21540657128279486
Iteration: 4610 ; error = 0.2892
TEST MSE - MF MSE: 0.21538997596427834
Iteration: 4620 ; error = 0.2892
TEST MSE - MF MSE: 0.2153717894637925
Iteration: 4630 ; error = 0.2892
TEST MSE - MF MSE: 0.2153552418161831
Iteration: 4640 ; error = 0.2892
TEST MSE - MF MSE: 0.21533918050056977
Iteration: 4650 ; error = 0.2892
TEST MSE - MF MSE: 0.21532623143189783
Iteration: 4660 ; error = 0.2892
TEST MSE - MF MSE: 0.21529048010864837
Iteration: 4670 ; error = 0.2891
TEST MSE - MF MSE: 0.21526346496263835
Iteration: 4680 ; error = 0.2891
TEST MSE - MF MSE: 0.2152506457347755
Iteration: 4690 ; error = 0.2891
TEST MSE - MF MSE: 0.2152571957605843
Iteration: 4700 ; error = 0.2891
TEST MSE - MF MSE: 0.2152186612799427
Iteration: 4710 ; error = 0.2891
TEST MSE - MF MSE: 0.2151886546828767
Iteration: 4720 ; error = 0.2891
TEST MSE - MF MSE: 0.2151717334135263
Iteration: 4730 ; error = 0.2890
TEST MSE - MF MSE: 0.21514566741396932
Iteration: 4740 ; error = 0.2890
TEST MSE - MF MSE: 0.21515315483022937
Iteration: 4750 ; error = 0.2890
TEST MSE - MF MSE: 0.2151450606861979
Iteration: 4760 ; error = 0.2890
TEST MSE - MF MSE: 0.21509716788113892
Iteration: 4770 ; error = 0.2890
TEST MSE - MF MSE: 0.21507691617411115
Iteration: 4780 ; error = 0.2890
TEST MSE - MF MSE: 0.2150855261831724
Iteration: 4790 ; error = 0.2889
TEST MSE - MF MSE: 0.21506167233204107
Iteration: 4800 ; error = 0.2889
TEST MSE - MF MSE: 0.2150265976960524
Iteration: 4810 ; error = 0.2889
TEST MSE - MF MSE: 0.21502364265927582
Iteration: 4820 ; error = 0.2889
TEST MSE - MF MSE: 0.21499464608857347
Iteration: 4830 ; error = 0.2889
TEST MSE - MF MSE: 0.21498724653125853
Iteration: 4840 ; error = 0.2889
TEST MSE - MF MSE: 0.2149863082359551
Iteration: 4850 ; error = 0.2888
TEST MSE - MF MSE: 0.21494735467606968
Iteration: 4860 ; error = 0.2888
TEST MSE - MF MSE: 0.21491335450786977
Iteration: 4870 ; error = 0.2888
TEST MSE - MF MSE: 0.2148929496368383
Iteration: 4880 ; error = 0.2888
TEST MSE - MF MSE: 0.2149002460867313
Iteration: 4890 ; error = 0.2888
TEST MSE - MF MSE: 0.21488747978691777
Iteration: 4900 ; error = 0.2888
TEST MSE - MF MSE: 0.21488127240818758
Iteration: 4910 ; error = 0.2888
TEST MSE - MF MSE: 0.21485699655818194
Iteration: 4920 ; error = 0.2887
TEST MSE - MF MSE: 0.21482329032553102
Iteration: 4930 ; error = 0.2887
TEST MSE - MF MSE: 0.2148169776608363
Iteration: 4940 ; error = 0.2887
TEST MSE - MF MSE: 0.21482118914229542
Iteration: 4950 ; error = 0.2887
TEST MSE - MF MSE: 0.2147918092620374
Iteration: 4960 ; error = 0.2887
TEST MSE - MF MSE: 0.21477061907800088
Iteration: 4970 ; error = 0.2887
TEST MSE - MF MSE: 0.21474272789669754
Iteration: 4980 ; error = 0.2886
TEST MSE - MF MSE: 0.21474232433956814
Iteration: 4990 ; error = 0.2886
TEST MSE - MF MSE: 0.21470618493415675
Iteration: 5000 ; error = 0.2886
TEST MSE - MF MSE: 0.21470171806043586

<<Compare>> - Matrix Factorisation vs Classification
|
|	<<Training>> - Classification: knn
|	<<Testing>> - Classification: knn
|
|	<<Training>> - Classification: decisionTree
|	<<Testing>> - Classification: decisionTree
|
|	<<Training>> - Classification: randomForest
|	<<Testing>> - Classification: randomForest

<<Compare>> - Matrix Factorisation vs Classification
Matrix Factorisation MSE: 0.21470171806043586
randomForest MSE:  1.325 | decisionTree MSE:  1.342 | knn MSE:  1.343 | 

<<Suggestion via classificatoin>>
For user [Age: 24, Gender: M], who works as technician...
("Nobody's Fool (1994)", '6.032')
('D3: The Mighty Ducks (1996)', '5.762')
('Quiet Man, The (1952)', '5.653')
('Legends of the Fall (1994)', '5.531')
('Cinema Paradiso (1988)', '5.527')

<<Suggestion via SVD>>
For user [Age: 24, Gender: M], who works as technician...
User 1 has already rated 10 movies.
Recommending highest 5 predicted ratings movies not already rated.
     item_id                 movie_title
7          8                 Babe (1995)
188      197        Graduate, The (1967)
740      751  Tomorrow Never Dies (1997)
394      405  Mission: Impossible (1996)
194      204   Back to the Future (1985)

<<Suggestion via uesr-based filtering>>
For user [Age: 24, Gender: M], who works as technician...
['Good, The Bad and The Ugly, The (1966)', 'Return of the Jedi (1983)', 'This Is Spinal Tap (1984)', 'Young Frankenstein (1974)', 'Piano, The (1993)']

<<Suggestion via item-based filtering>>
Because you liked <Toy Story (1995)>, we'd recommend you to watch...
['Metro (1997)', 'Loch Ness (1995)', 'Long Kiss Goodnight, The (1996)', 'Waiting to Exhale (1995)', 'Mission: Impossible (1996)', 'Toy Story (1995)']