Estimator unexpectedly predicting empty class labels
MarkLuds opened this issue · comments
Hi, maybe this is meant to happen not sure. sometimes when i add more features or change the alpha 'L2 regularization', random new classes will be created. I'm assuming that this is happening because the weights are being updated too quickly. I usually only have 2 classes (win, lose). In the example below i add in 3 new features and this results in new classes being made, one with no label and another with the label 0. In the example where the classes are created, the added features are substantially larger than the current features, though would be this way for all samples.
so my question is, is it normal that these new classes are created when changing only features or L2 regularization?
Thanks, Mark
the original features:
(
[0] => -0.18295004568992
[1] => -0.83372677429181
[2] => -0.36064575083765
[3] => 0.51926591532135
[4] => -0.72959183673469
[5] => 0.1991318915626
[6] => -0.80741699664941
[7] => -0.14860645750838
[8] => -0.099870545233018
[9] => -0.18188394760889
[10] => -0.38790740176668
[11] => -0.66406487968322
[12] => -0.39487511422479
[13] => -0.60702863234846
[14] => 1
[15] => -0.78925525434054
[16] => -0.090123362777946
[17] => -0.46497106305209
[18] => -1
[19] => 0.072151995126408
[20] => -0.5138592750533
[21] => -0.63379530916844
[22] => -0.78156411818459
[23] => 0.037884556807799
[24] => -0.13745050258909
[25] => -0.13745050258909
[26] => -0.13745050258909
[27] => -0.13745050258909
[28] => -0.13745050258909
[29] => -0.13745050258909
[30] => -0.13745050258909
[31] => -5.2436368202698
[32] => -0.86024122206422
[33] => 1.5325879408103
[34] => 0.65829640938086
[35] => 0.64121041761968
[36] => 0.63684030714221
[37] => 0.62422410521904
[38] => 0.58765122132457
)
the result of the origional features:
Rubix\ML\Report Object
(
[attributes:protected] => Array
(
[0] => Rubix\ML\Report Object
(
[attributes:protected] => Array
(
[overall] => Array
(
[accuracy] => 0.70227808326787
[balanced accuracy] => 0.70227808326787
[f1 score] => 0.69965350405387
[precision] => 0.70960460647682
[recall] => 0.70227808326787
[specificity] => 0.70227808326787
[negative predictive value] => 0.70960460647682
[false discovery rate] => 0.29039539352318
[miss rate] => 0.29772191673213
[fall out] => 0.29772191673213
[false omission rate] => 0.29039539352318
[mcc] => 0.41181752290182
[informedness] => 0.40455616653574
[markedness] => 0.41920921295364
[true positives] => 3576
[true negatives] => 3576
[false positives] => 1516
[false negatives] => 1516
[cardinality] => 5092
)
[classes] => Array
(
[win] => Array
(
[accuracy] => 0.70227808326787
[balanced accuracy] => 0.70227808326787
[f1 score] => 0.67157712305026
[precision] => 0.7487922705314
[recall] => 0.60879811468971
[specificity] => 0.79575805184603
[negative predictive value] => 0.67041694242224
[false discovery rate] => 0.2512077294686
[miss rate] => 0.39120188531029
[fall out] => 0.20424194815397
[false omission rate] => 0.32958305757776
[informedness] => 0.40455616653574
[markedness] => 0.41920921295364
[mcc] => 0.41181752290182
[true positives] => 1550
[true negatives] => 2026
[false positives] => 520
[false negatives] => 996
[cardinality] => 2546
[proportion] => 0.5
)
[lose] => Array
(
[accuracy] => 0.70227808326787
[balanced accuracy] => 0.70227808326787
[f1 score] => 0.72772988505747
[precision] => 0.67041694242224
[recall] => 0.79575805184603
[specificity] => 0.60879811468971
[negative predictive value] => 0.7487922705314
[false discovery rate] => 0.32958305757776
[miss rate] => 0.20424194815397
[fall out] => 0.39120188531029
[false omission rate] => 0.2512077294686
[informedness] => 0.40455616653574
[markedness] => 0.41920921295364
[mcc] => 0.41181752290182
[true positives] => 2026
[true negatives] => 1550
[false positives] => 996
[false negatives] => 520
[cardinality] => 2546
[proportion] => 0.5
)
)
)
)
[1] => Rubix\ML\Report Object
(
[attributes:protected] => Array
(
[win] => Array
(
[win] => 1550
[lose] => 520
)
[lose] => Array
(
[win] => 996
[lose] => 2026
)
)
)
)
)
next added in 34, 35, 36 to the features array. these values are much higher > 30, so perhaps upset the weights.
(
[0] => -0.18295004568992
[1] => -0.83372677429181
[2] => -0.36064575083765
[3] => 0.51926591532135
[4] => -0.72959183673469
[5] => 0.1991318915626
[6] => -0.80741699664941
[7] => -0.14860645750838
[8] => -0.099870545233018
[9] => -0.18188394760889
[10] => -0.38790740176668
[11] => -0.66406487968322
[12] => -0.39487511422479
[13] => -0.60702863234846
[14] => 1
[15] => -0.78925525434054
[16] => -0.090123362777946
[17] => -0.46497106305209
[18] => -1
[19] => 0.072151995126408
[20] => -0.5138592750533
[21] => -0.63379530916844
[22] => -0.78156411818459
[23] => 0.037884556807799
[24] => -0.13745050258909
[25] => -0.13745050258909
[26] => -0.13745050258909
[27] => -0.13745050258909
[28] => -0.13745050258909
[29] => -0.13745050258909
[30] => -0.13745050258909
[31] => -5.2436368202698
[32] => -0.86024122206422
[33] => 1.5325879408103
[34] => 40.947888842454
[35] => 15.049572702343
[36] => 28.375953124697
[37] => 0.65829640938086
[38] => 0.64121041761968
[39] => 0.63684030714221
[40] => 0.62422410521904
[41] => 0.58765122132457
)
This Results in the following. Here there are now 4 classes.
Rubix\ML\Report Object
(
[attributes:protected] => Array
(
[0] => Rubix\ML\Report Object
(
[attributes:protected] => Array
(
[overall] => Array
(
[accuracy] => 0.42873043933739
[balanced accuracy] => 0.42873045587902
[f1 score] => 0.40974282541048
[precision] => 0.46890839533503
[recall] => 0.42864624247185
[specificity] => 0.42881466928618
[negative predictive value] => 0.46862462572938
[false discovery rate] => 0.53109160466497
[miss rate] => 0.57135375752815
[fall out] => 0.57118533071382
[false omission rate] => 0.53137537427062
[mcc] => 0.22733178488711
[informedness] => -0.14253908824197
[markedness] => -0.062466978935593
[true positives] => 3274
[true negatives] => 3274
[false positives] => 1816
[false negatives] => 1818
[cardinality] => 5092
)
[classes] => Array
(
[win] => Array
(
[accuracy] => 0.64309565900609
[balanced accuracy] => 0.64314908242368
[f1 score] => 0.5098462368492
[precision] => 0.81395348837209
[recall] => 0.37117046347211
[specificity] => 0.91512770137525
[negative predictive value] => 0.59262086513995
[false discovery rate] => 0.18604651162791
[miss rate] => 0.62882953652789
[fall out] => 0.084872298624754
[false omission rate] => 0.40737913486005
[informedness] => 0.28629816484736
[markedness] => 0.40657435351204
[mcc] => 0.34117662769378
[true positives] => 945
[true negatives] => 2329
[false positives] => 216
[false negatives] => 1601
[cardinality] => 2546
[proportion] => 0.5
)
[lose] => Array
(
[accuracy] => 0.64309565900609
[balanced accuracy] => 0.64304228521337
[f1 score] => 0.71938223938224
[precision] => 0.59277169763299
[recall] => 0.91476826394344
[specificity] => 0.3713163064833
[negative predictive value] => 0.81325301204819
[false discovery rate] => 0.40722830236701
[miss rate] => 0.085231736056559
[fall out] => 0.6286836935167
[false omission rate] => 0.18674698795181
[informedness] => 0.28608457042674
[markedness] => 0.40602470968118
[mcc] => 0.34081872696755
[true positives] => 2329
[true negatives] => 945
[false positives] => 1600
[false negatives] => 217
[cardinality] => 2546
[proportion] => 0.5
)
[] => Array
(
[accuracy] => 0
[balanced accuracy] => 0
[f1 score] => 0
[precision] => 0
[recall] => 0
[specificity] => 0
[negative predictive value] => 0
[false discovery rate] => 1
[miss rate] => 1
[fall out] => 1
[false omission rate] => 1
[informedness] => -1
[markedness] => -1
[mcc] => 0
[true positives] => 0
[true negatives] => 0
[false positives] => 0
[false negatives] => 0
[cardinality] => 0
[proportion] => 0
)
)
)
)
[1] => Rubix\ML\Report Object
(
[attributes:protected] => Array
(
[win] => Array
(
[win] => 945
[lose] => 216
[] => 0
)
[lose] => Array
(
[win] => 1600
[lose] => 2329
[] => 0
)
[] => Array
(
[win] => 0
[lose] => 0
[] => 0
)
[0] => Array
(
[win] => 1
[lose] => 1
)
)
)
)
)
here is my model:
[2022-02-13 17:46:17] INFO: Multilayer Perceptron (hidden layers: [0: Dense (neurons: 39, alpha: 1.0E-5, bias: true, weight initializer: He, bias initializer: Constant (value: 0)), 1: Activation (activation fn: ELU (alpha: 2.5)), 2: Batch Norm (decay: 0.3, beta initializer: Constant (value: 0), gamma initializer: Normal (std_dev: 1)), 3: Dense (neurons: 39, alpha: 1.0E-5, bias: true, weight initializer: He, bias initializer: Constant (value: 0)), 4: Activation (activation fn: ELU (alpha: 2.5)), 5: Batch Norm (decay: 0.3, beta initializer: Constant (value: 0), gamma initializer: Normal (std_dev: 1)), 6: Dense (neurons: 39, alpha: 1.0E-5, bias: true, weight initializer: He, bias initializer: Constant (value: 0)), 7: PReLU (initializer: Normal (std_dev: 0.25))], batch size: 128, optimizer: AdaMax (rate: 0.0001, momentum_decay: 0.02, norm_decay: 0.999), alpha: 0.0001, epochs: 20, min change: 1.0E-8, window: 20, hold out: 0, cost fn: Cross Entropy, metric: MCC)
versions:
rubix/ml 1.3.1
rubix/tensor 3.0.1
I've seen this before and yes I do believe it is related to the scale of the new features causing instability. The extra "classes" 0 and null/"" I think if I recall correctly are due to the way PHP inserts NANs or booleans into an array as an index. It's an unintended side effect. I'll have to look more into it to find out exactly why that happens.
With neural nets it's always a good idea to standardize the features as a preprocessing step. I would try running the same features but after applying something like Z Scale Standardizer beforehand. Remember to standardize both the training and testing sets.
https://docs.rubixml.com/1.0/transformers/z-scale-standardizer.html
It looks like the extra "classes" are being added somewhere around here https://github.com/RubixML/ML/blob/master/src/CrossValidation/Reports/MulticlassBreakdown.php#L67
Can you dump the array containing the $predictions and $labels separately so that we can take a closer look?
Also, just as a preliminary check, can you make sure you don't have any blank ("") labels in either your training or testing sets?
print_r($dataset->describe());Hey, great thanks, i'll check out that scaling transformer.
I've dumped all of the arrays in here, so let me know if they are annoyingly large and i'll edit them down. You can skip between them if you search for "training labels", "testing labels", "predictions", "testing dataset"
This is the description of the training data. here some of the min/max values are really big, so i guess that's creating a problem.
Rubix\ML\Report Object
(
[attributes:protected] => Array
(
[0] => Array
(
[offset] => 0
[type] => continuous
[mean] => 0.073158336241229
[standard deviation] => 0.59278752022008
[skewness] => 0.18050459206108
[kurtosis] => -1.1060024598621
[min] => -1
[25%] => -0.40437259494097
[median] => -0.010743115488011
[75%] => 0.56462256866081
[max] => 1
)
[1] => Array
(
[offset] => 1
[type] => continuous
[mean] => 0.072887712820359
[standard deviation] => 0.59274961805198
[skewness] => 0.18182032626333
[kurtosis] => -1.108444696689
[min] => -1
[25%] => -0.40518174207871
[median] => -0.013052036343245
[75%] => 0.56438053156084
[max] => 1
)
[2] => Array
(
[offset] => 2
[type] => continuous
[mean] => 0.076486215303728
[standard deviation] => 0.58949103578917
[skewness] => 0.18456694691724
[kurtosis] => -1.1130568082275
[min] => -1
[25%] => -0.40241150201391
[median] => -0.0076349423685047
[75%] => 0.56632698064078
[max] => 1
)
[3] => Array
(
[offset] => 3
[type] => continuous
[mean] => 0.074519155189057
[standard deviation] => 0.59143782762688
[skewness] => 0.18520002003572
[kurtosis] => -1.1111623693135
[min] => -1
[25%] => -0.40317957628131
[median] => -0.010463522596028
[75%] => 0.56553091187756
[max] => 1
)
[4] => Array
(
[offset] => 4
[type] => continuous
[mean] => 0.074125991698316
[standard deviation] => 0.58934816356009
[skewness] => 0.19032267601938
[kurtosis] => -1.1077754868988
[min] => -1
[25%] => -0.40306057345846
[median] => -0.011357426720759
[75%] => 0.56169735639559
[max] => 1
)
[5] => Array
(
[offset] => 5
[type] => continuous
[mean] => 0.07563656228373
[standard deviation] => 0.58736708642607
[skewness] => 0.19147678613828
[kurtosis] => -1.1026457857705
[min] => -1
[25%] => -0.40002777663832
[median] => -0.009106152090924
[75%] => 0.55933070764148
[max] => 1
)
[6] => Array
(
[offset] => 6
[type] => continuous
[mean] => 0.075097019737031
[standard deviation] => 0.58763086813308
[skewness] => 0.19098448629755
[kurtosis] => -1.1034697476034
[min] => -1
[25%] => -0.40057109091926
[median] => -0.0088976455533206
[75%] => 0.56020068186145
[max] => 1
)
[7] => Array
(
[offset] => 7
[type] => continuous
[mean] => 0.074173307011829
[standard deviation] => 0.58783674511298
[skewness] => 0.19150741440264
[kurtosis] => -1.1006862794473
[min] => -1
[25%] => -0.40171625011792
[median] => -0.0109137686648
[75%] => 0.55921851727334
[max] => 1
)
[8] => Array
(
[offset] => 8
[type] => continuous
[mean] => -0.026850110890546
[standard deviation] => 0.77348242417533
[skewness] => 0.075390230356457
[kurtosis] => -1.5629839350193
[min] => -1
[25%] => -0.83943191743022
[median] => -0.06085617559787
[75%] => 0.80484782387103
[max] => 1
)
[9] => Array
(
[offset] => 9
[type] => continuous
[mean] => -0.11109153767878
[standard deviation] => 0.66579899668259
[skewness] => 0.23176240313133
[kurtosis] => -1.2707062919653
[min] => -1
[25%] => -0.73093710006024
[median] => -0.17556707764978
[75%] => 0.46238225582278
[max] => 1
)
[10] => Array
(
[offset] => 10
[type] => continuous
[mean] => -0.14446830099695
[standard deviation] => 0.61837604484296
[skewness] => 0.35399030090261
[kurtosis] => -1.0177645256259
[min] => -1
[25%] => -0.67438132227174
[median] => -0.22450307489692
[75%] => 0.33241647592402
[max] => 1
)
[11] => Array
(
[offset] => 11
[type] => continuous
[mean] => -0.16840792106228
[standard deviation] => 0.5923320227946
[skewness] => 0.40780665107476
[kurtosis] => -0.85709703531617
[min] => -1
[25%] => -0.65912199187375
[median] => -0.24576309223928
[75%] => 0.25919308316194
[max] => 1
)
[12] => Array
(
[offset] => 12
[type] => continuous
[mean] => -0.18141398104781
[standard deviation] => 0.57862273983947
[skewness] => 0.41879698481403
[kurtosis] => -0.78759807809372
[min] => -1
[25%] => -0.65989082831402
[median] => -0.24951124863536
[75%] => 0.2273941402201
[max] => 1
)
[13] => Array
(
[offset] => 13
[type] => continuous
[mean] => -0.17961244661334
[standard deviation] => 0.58507725312153
[skewness] => 0.39951608788894
[kurtosis] => -0.84723770695581
[min] => -1
[25%] => -0.6750838601884
[median] => -0.24565067548312
[75%] => 0.23980314876669
[max] => 1
)
[14] => Array
(
[offset] => 14
[type] => continuous
[mean] => -0.18499667683254
[standard deviation] => 0.601457334065
[skewness] => 0.38846238819523
[kurtosis] => -0.94078857819941
[min] => -1
[25%] => -0.7053644961375
[median] => -0.26113543404189
[75%] => 0.27176743931142
[max] => 1
)
[15] => Array
(
[offset] => 15
[type] => continuous
[mean] => -0.16489101636419
[standard deviation] => 0.6350170590341
[skewness] => 0.33066137351541
[kurtosis] => -1.1352779060633
[min] => -1
[25%] => -0.7440459523763
[median] => -0.24525462195734
[75%] => 0.35262245494872
[max] => 1
)
[16] => Array
(
[offset] => 16
[type] => continuous
[mean] => -0.14381375426929
[standard deviation] => 0.68495444453427
[skewness] => 0.2831685389879
[kurtosis] => -1.3183993735176
[min] => -1
[25%] => -0.80575797274956
[median] => -0.24041950029283
[75%] => 0.46691341669247
[max] => 1
)
[17] => Array
(
[offset] => 17
[type] => continuous
[mean] => -0.12298396872957
[standard deviation] => 0.7589842740407
[skewness] => 0.26501788981002
[kurtosis] => -1.4775590187994
[min] => -1
[25%] => -0.90677150337106
[median] => -0.25581395348837
[75%] => 0.6317964437051
[max] => 1
)
[18] => Array
(
[offset] => 18
[type] => continuous
[mean] => 10.016284105674
[standard deviation] => 5.6837465805299
[skewness] => 2.7357687757698
[kurtosis] => 24.321122533369
[min] => 1.0E-8
[25%] => 6
[median] => 9
[75%] => 12
[max] => 141
)
[19] => Array
(
[offset] => 19
[type] => continuous
[mean] => 20.091598094423
[standard deviation] => 8.5359791101318
[skewness] => 3.3305018479522
[kurtosis] => 35.567813634199
[min] => 1.0E-8
[25%] => 14
[median] => 18
[75%] => 24
[max] => 168
)
[20] => Array
(
[offset] => 20
[type] => continuous
[mean] => 30.098007795607
[standard deviation] => 10.646733412916
[skewness] => 3.2078673443585
[kurtosis] => 31.719868335951
[min] => 1.0E-8
[25%] => 23
[median] => 28
[75%] => 35
[max] => 188
)
[21] => Array
(
[offset] => 21
[type] => continuous
[mean] => 40.050498051187
[standard deviation] => 12.139482986044
[skewness] => 2.5715532250001
[kurtosis] => 21.355046072269
[min] => 1.0E-8
[25%] => 32
[median] => 38
[75%] => 46
[max] => 195
)
[22] => Array
(
[offset] => 22
[type] => continuous
[mean] => 49.892334374536
[standard deviation] => 13.255996743164
[skewness] => 1.6554779860471
[kurtosis] => 9.4811296474573
[min] => 1.0E-8
[25%] => 41
[median] => 48
[75%] => 57
[max] => 195
)
[23] => Array
(
[offset] => 23
[type] => continuous
[mean] => 10.262797747943
[standard deviation] => 6.1215681408843
[skewness] => 3.2869281116284
[kurtosis] => 34.006386640472
[min] => 4
[25%] => 6
[median] => 9
[75%] => 13
[max] => 144
)
[24] => Array
(
[offset] => 24
[type] => continuous
[mean] => 20.64859246427
[standard deviation] => 8.7526044611285
[skewness] => 2.8727059189486
[kurtosis] => 28.302380195735
[min] => 8
[25%] => 15
[median] => 19
[75%] => 25
[max] => 189
)
[25] => Array
(
[offset] => 25
[type] => continuous
[mean] => 30.956344744483
[standard deviation] => 10.599480253569
[skewness] => 2.0021289572707
[kurtosis] => 13.025821629409
[min] => 4.0E-8
[25%] => 24
[median] => 29
[75%] => 36
[max] => 174
)
[26] => Array
(
[offset] => 26
[type] => continuous
[mean] => 41.320788226515
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[min] => 4.0E-8
[25%] => 33
[median] => 40
[75%] => 48
[max] => 191
)
[27] => Array
(
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[max] => 195
)
[28] => Array
(
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[29] => Array
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[30] => Array
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[33] => Array
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[35] => Array
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[max] => 1
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[36] => Array
(
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[mean] => 0.75884709803968
[standard deviation] => 0.33617566031173
[skewness] => -2.7198264296161
[kurtosis] => 8.0122872889507
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[median] => 0.88613345653264
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[max] => 1
)
[37] => Array
(
[offset] => 37
[type] => continuous
[mean] => 0.75802027779007
[standard deviation] => 0.33443604926285
[skewness] => -2.7038026135237
[kurtosis] => 7.910577585936
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[median] => 0.88467619651903
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[max] => 1
)
[38] => Array
(
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[type] => continuous
[mean] => 0.75724122095796
[standard deviation] => 0.3336427309714
[skewness] => -2.696263623649
[kurtosis] => 7.8668853299441
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[median] => 0.88356943671922
[75%] => 0.94352153462547
[max] => 1
)
[39] => Array
(
[offset] => 39
[type] => categorical
[num categories] => 2
[probabilities] => Array
(
[lose] => 0.5
[win] => 0.5
)
)
)
)
here is the description of the testing dataset. Same thing with the large min/max.
Rubix\ML\Report Object
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[4] => Array
(
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[mean] => 0.11678434663787
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[median] => 0.047794028916663
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[5] => Array
(
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[kurtosis] => -1.1646973264894
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[median] => 0.04600621631371
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[6] => Array
(
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[8] => Array
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[max] => 1
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[9] => Array
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[type] => continuous
[mean] => -0.15737719619362
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[skewness] => 0.35350455609528
[kurtosis] => -1.1945471117287
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[max] => 1
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[10] => Array
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[kurtosis] => -0.89694894606074
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[11] => Array
(
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[median] => -0.30854733205743
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[max] => 1
)
[12] => Array
(
[offset] => 12
[type] => continuous
[mean] => -0.22324154739825
[standard deviation] => 0.57845377483467
[skewness] => 0.53443178858516
[kurtosis] => -0.65723045238111
[min] => -1
[25%] => -0.69753601051795
[median] => -0.30625499458879
[75%] => 0.16676572645241
[max] => 1
)
[13] => Array
(
[offset] => 13
[type] => continuous
[mean] => -0.19900698020017
[standard deviation] => 0.59307811785355
[skewness] => 0.43970929114051
[kurtosis] => -0.84167377142946
[min] => -1
[25%] => -0.70075980830038
[median] => -0.28145260811803
[75%] => 0.23588648428988
[max] => 1
)
[14] => Array
(
[offset] => 14
[type] => continuous
[mean] => -0.17028942207244
[standard deviation] => 0.62159641620111
[skewness] => 0.35522967509136
[kurtosis] => -1.0237628456416
[min] => -1
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[max] => 1
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[15] => Array
(
[offset] => 15
[type] => continuous
[mean] => -0.15108679192773
[standard deviation] => 0.63842075298104
[skewness] => 0.28860619923389
[kurtosis] => -1.1664203312383
[min] => -1
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[median] => -0.21613588518266
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[max] => 1
)
[16] => Array
(
[offset] => 16
[type] => continuous
[mean] => -0.11968868560352
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[skewness] => 0.2275043586754
[kurtosis] => -1.3516275315534
[min] => -1
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[median] => -0.20296739676037
[75%] => 0.49494478156521
[max] => 1
)
[17] => Array
(
[offset] => 17
[type] => continuous
[mean] => -0.084742545472565
[standard deviation] => 0.76906198409391
[skewness] => 0.19914359888912
[kurtosis] => -1.5339992211115
[min] => -1
[25%] => -0.88134980009321
[median] => -0.21520002023908
[75%] => 0.73143663239437
[max] => 1
)
[18] => Array
(
[offset] => 18
[type] => continuous
[mean] => 9.9003489724719
[standard deviation] => 6.423812744527
[skewness] => 7.9369229190317
[kurtosis] => 163.50687455653
[min] => 1.0E-8
[25%] => 6
[median] => 9
[75%] => 12
[max] => 183
)
[19] => Array
(
[offset] => 19
[type] => continuous
[mean] => 19.943582784802
[standard deviation] => 9.1136525467757
[skewness] => 4.5961747921423
[kurtosis] => 53.891228883765
[min] => 1.0E-8
[25%] => 14
[median] => 18
[75%] => 24
[max] => 175
)
[20] => Array
(
[offset] => 20
[type] => continuous
[mean] => 30.109344708028
[standard deviation] => 11.641273870882
[skewness] => 4.4670452047826
[kurtosis] => 45.803260157726
[min] => 1.0E-8
[25%] => 23
[median] => 28
[75%] => 35
[max] => 190
)
[21] => Array
(
[offset] => 21
[type] => continuous
[mean] => 39.999418398113
[standard deviation] => 13.310260923173
[skewness] => 3.6575761919551
[kurtosis] => 32.095715013751
[min] => 1.0E-8
[25%] => 32
[median] => 38
[75%] => 46
[max] => 194
)
[22] => Array
(
[offset] => 22
[type] => continuous
[mean] => 49.724893388816
[standard deviation] => 14.398772982294
[skewness] => 2.3885012765667
[kurtosis] => 17.362537206798
[min] => 1.0E-8
[25%] => 41
[median] => 48
[75%] => 56.75
[max] => 195
)
[23] => Array
(
[offset] => 23
[type] => continuous
[mean] => 10.622528111673
[standard deviation] => 9.0526663570682
[skewness] => 9.9724368750061
[kurtosis] => 175.80788301501
[min] => 1.0E-8
[25%] => 6
[median] => 9
[75%] => 13
[max] => 194
)
[24] => Array
(
[offset] => 24
[type] => continuous
[mean] => 20.879216750741
[standard deviation] => 10.386200392488
[skewness] => 3.4705788857538
[kurtosis] => 23.558804981748
[min] => 1.0E-11
[25%] => 14
[median] => 19
[75%] => 25
[max] => 147
)
[25] => Array
(
[offset] => 25
[type] => continuous
[mean] => 31.265219079248
[standard deviation] => 12.623543644129
[skewness] => 2.8291333686977
[kurtosis] => 16.640181784757
[min] => 1.0E-11
[25%] => 23.25
[median] => 29
[75%] => 36
[max] => 157
)
[26] => Array
(
[offset] => 26
[type] => continuous
[mean] => 41.635517644584
[standard deviation] => 14.875278123059
[skewness] => 2.4628861202357
[kurtosis] => 12.637209441448
[min] => 1.0E-11
[25%] => 32
[median] => 39
[75%] => 47
[max] => 177
)
[27] => Array
(
[offset] => 27
[type] => continuous
[mean] => 51.800116326408
[standard deviation] => 16.339475642578
[skewness] => 1.9356894376702
[kurtosis] => 9.5936728639169
[min] => 1.0E-11
[25%] => 42
[median] => 49
[75%] => 59
[max] => 192
)
[28] => Array
(
[offset] => 28
[type] => continuous
[mean] => -12.590552220905
[standard deviation] => 324.92936692596
[skewness] => -44.977932477856
[kurtosis] => 2110.5965159998
[min] => -16658.68484041
[25%] => -7.9201126400017
[median] => -1.9650024011454
[75%] => 2.5548857571216
[max] => 33.803354251452
)
[29] => Array
(
[offset] => 29
[type] => continuous
[mean] => -3.1558775105164
[standard deviation] => 119.38809347458
[skewness] => -62.521597631184
[kurtosis] => 4165.7416179774
[min] => -8120.1157416874
[25%] => -3.9947308971127
[median] => -0.70725285233331
[75%] => 2.7820544021378
[max] => 32.851426828584
)
[30] => Array
(
[offset] => 30
[type] => continuous
[mean] => 0.14511626605148
[standard deviation] => 22.159976819454
[skewness] => -68.989214980257
[kurtosis] => 4886.3907116052
[min] => -1570.2291288259
[25%] => -1.7337927689347
[median] => -0.12743682129894
[75%] => 2.1652657316866
[max] => 30.107281721718
)
[31] => Array
(
[offset] => 31
[type] => continuous
[mean] => -0.10252617293525
[standard deviation] => 0.5250983371347
[skewness] => -0.12248317300416
[kurtosis] => -0.55378870308789
[min] => -1
[25%] => -0.45
[median] => -0.02
[75%] => 0.22
[max] => 1
)
[32] => Array
(
[offset] => 32
[type] => continuous
[mean] => 28.621936356442
[standard deviation] => 25.147520221035
[skewness] => 0.88153815776743
[kurtosis] => -0.18104664446397
[min] => 0
[25%] => 7.3204172247982
[median] => 21.367523972256
[75%] => 44.936006353526
[max] => 100
)
[33] => Array
(
[offset] => 33
[type] => continuous
[mean] => 34.494940218899
[standard deviation] => 26.524100429816
[skewness] => 0.58881607635933
[kurtosis] => -0.71819398260898
[min] => 0
[25%] => 11.29121140994
[median] => 28.915540994246
[75%] => 54.248747272573
[max] => 100
)
[34] => Array
(
[offset] => 34
[type] => continuous
[mean] => 0.77741684098507
[standard deviation] => 0.31685228142897
[skewness] => -2.9123410356797
[kurtosis] => 9.891816253491
[min] => -1
[25%] => 0.75256834147617
[median] => 0.89297911813324
[75%] => 0.95041417881894
[max] => 1
)
[35] => Array
(
[offset] => 35
[type] => continuous
[mean] => 0.77607334452162
[standard deviation] => 0.31368448733586
[skewness] => -2.8668091947568
[kurtosis] => 9.4756426091357
[min] => -1
[25%] => 0.75106479505886
[median] => 0.89156676103273
[75%] => 0.94869301913728
[max] => 1
)
[36] => Array
(
[offset] => 36
[type] => continuous
[mean] => 0.77477839588871
[standard deviation] => 0.31149906200191
[skewness] => -2.8170322080284
[kurtosis] => 9.0433381244472
[min] => -1
[25%] => 0.75101706585277
[median] => 0.89078059494632
[75%] => 0.9459795027421
[max] => 1
)
[37] => Array
(
[offset] => 37
[type] => continuous
[mean] => 0.77340738690127
[standard deviation] => 0.30953509557455
[skewness] => -2.7864730660301
[kurtosis] => 8.8334646112434
[min] => -1
[25%] => 0.74971959855052
[median] => 0.88872572516883
[75%] => 0.94445985673072
[max] => 1
)
[38] => Array
(
[offset] => 38
[type] => continuous
[mean] => 0.77257737130506
[standard deviation] => 0.30754970226268
[skewness] => -2.7622035399736
[kurtosis] => 8.6733758543893
[min] => -1
[25%] => 0.74937009629357
[median] => 0.88647977051344
[75%] => 0.94371054054496
[max] => 1
)
[39] => Array
(
[offset] => 39
[type] => categorical
[num categories] => 2
[probabilities] => Array
(
[lose] => 0.5
[win] => 0.5
)
)
)
)
Okay here are the predictions. All are empty.
Array
(
[0] =>
[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] =>
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[4884] =>
[4885] =>
[4886] =>
[4887] =>
[4888] =>
[4889] =>
[4890] =>
[4891] =>
[4892] =>
[4893] =>
[4894] =>
[4895] =>
[4896] =>
[4897] =>
[4898] =>
[4899] =>
[4900] =>
[4901] =>
[4902] =>
[4903] =>
[4904] =>
[4905] =>
[4906] =>
[4907] =>
[4908] =>
[4909] =>
[4910] =>
[4911] =>
[4912] =>
[4913] =>
[4914] =>
[4915] =>
[4916] =>
[4917] =>
[4918] =>
[4919] =>
[4920] =>
[4921] =>
[4922] =>
[4923] =>
[4924] =>
[4925] =>
[4926] =>
[4927] =>
[4928] =>
[4929] =>
[4930] =>
[4931] =>
[4932] =>
[4933] =>
[4934] =>
[4935] =>
[4936] =>
[4937] =>
[4938] =>
[4939] =>
[4940] =>
[4941] =>
[4942] =>
[4943] =>
[4944] =>
[4945] =>
[4946] =>
[4947] =>
[4948] =>
[4949] =>
[4950] =>
[4951] =>
[4952] =>
[4953] =>
[4954] =>
[4955] =>
[4956] =>
[4957] =>
[4958] =>
[4959] =>
[4960] =>
[4961] =>
[4962] =>
[4963] =>
[4964] =>
[4965] =>
[4966] =>
[4967] =>
[4968] =>
[4969] =>
[4970] =>
[4971] =>
[4972] =>
[4973] =>
[4974] =>
[4975] =>
[4976] =>
[4977] =>
[4978] =>
[4979] =>
[4980] =>
[4981] =>
[4982] =>
[4983] =>
[4984] =>
[4985] =>
[4986] =>
[4987] =>
[4988] =>
[4989] =>
[4990] =>
[4991] =>
[4992] =>
[4993] =>
[4994] =>
[4995] =>
[4996] =>
[4997] =>
[4998] =>
[4999] =>
[5000] =>
[5001] =>
[5002] =>
[5003] =>
[5004] =>
[5005] =>
[5006] =>
[5007] =>
[5008] =>
[5009] =>
[5010] =>
[5011] =>
[5012] =>
[5013] =>
[5014] =>
[5015] =>
[5016] =>
[5017] =>
[5018] =>
[5019] =>
[5020] =>
[5021] =>
[5022] =>
[5023] =>
[5024] =>
[5025] =>
[5026] =>
[5027] =>
[5028] =>
[5029] =>
[5030] =>
[5031] =>
[5032] =>
[5033] =>
[5034] =>
[5035] =>
[5036] =>
[5037] =>
[5038] =>
[5039] =>
[5040] =>
[5041] =>
[5042] =>
[5043] =>
[5044] =>
[5045] =>
[5046] =>
[5047] =>
[5048] =>
[5049] =>
[5050] =>
[5051] =>
[5052] =>
[5053] =>
[5054] =>
[5055] =>
[5056] =>
[5057] =>
[5058] =>
[5059] =>
[5060] =>
[5061] =>
[5062] =>
[5063] =>
[5064] =>
[5065] =>
[5066] =>
[5067] =>
[5068] =>
[5069] =>
[5070] =>
[5071] =>
[5072] =>
[5073] =>
[5074] =>
[5075] =>
[5076] =>
[5077] =>
[5078] =>
[5079] =>
[5080] =>
[5081] =>
[5082] =>
[5083] =>
[5084] =>
[5085] =>
[5086] =>
[5087] =>
[5088] =>
[5089] =>
[5090] =>
[5091] =>
[5092] =>
[5093] =>
[5094] =>
[5095] =>
[5096] =>
[5097] =>
[5098] =>
[5099] =>
[5100] =>
[5101] =>
[5102] =>
[5103] =>
[5104] =>
[5105] =>
[5106] =>
[5107] =>
[5108] =>
[5109] =>
[5110] =>
[5111] =>
[5112] =>
[5113] =>
[5114] =>
[5115] =>
[5116] =>
[5117] =>
[5118] =>
[5119] =>
[5120] =>
[5121] =>
[5122] =>
[5123] =>
[5124] =>
[5125] =>
[5126] =>
[5127] =>
[5128] =>
[5129] =>
[5130] =>
[5131] =>
[5132] =>
[5133] =>
[5134] =>
[5135] =>
[5136] =>
[5137] =>
[5138] =>
[5139] =>
[5140] =>
[5141] =>
[5142] =>
[5143] =>
[5144] =>
[5145] =>
[5146] =>
[5147] =>
[5148] =>
[5149] =>
[5150] =>
[5151] =>
[5152] =>
[5153] =>
[5154] =>
[5155] =>
[5156] =>
[5157] =>
)
this is the training labels. it's too big for the comment, but there is just win/lose labels the whole way through:
[22780] => win
[22781] => lose
[22782] => lose
[22783] => win
[22784] => lose
[22785] => lose
[22786] => lose
[22787] => win
[22788] => lose
[22789] => win
[22790] => win
[22791] => win
[22792] => win
[22793] => lose
[22794] => win
[22795] => lose
[22796] => lose
[22797] => lose
[22798] => lose
[22799] => win
[22800] => win
[22801] => lose
[22802] => win
[22803] => lose
[22804] => lose
[22805] => win
[22806] => win
[22807] => lose
[22808] => win
[22809] => lose
[22810] => lose
[22811] => win
[22812] => lose
[22813] => win
[22814] => win
[22815] => lose
[22816] => lose
[22817] => lose
[22818] => win
[22819] => win
[22820] => win
[22821] => lose
[22822] => lose
[22823] => win
[22824] => lose
[22825] => win
[22826] => win
[22827] => win
[22828] => lose
[22829] => lose
[22830] => lose
[22831] => lose
[22832] => lose
[22833] => win
[22834] => lose
[22835] => lose
[22836] => lose
[22837] => win
[22838] => win
[22839] => lose
[22840] => win
[22841] => lose
[22842] => lose
[22843] => win
[22844] => win
[22845] => lose
[22846] => lose
[22847] => win
[22848] => win
[22849] => lose
[22850] => win
[22851] => win
[22852] => win
[22853] => win
[22854] => win
[22855] => lose
[22856] => win
[22857] => win
[22858] => lose
[22859] => lose
[22860] => win
[22861] => win
[22862] => lose
[22863] => win
[22864] => win
[22865] => win
[22866] => lose
[22867] => lose
[22868] => win
[22869] => win
[22870] => lose
[22871] => lose
[22872] => win
[22873] => win
[22874] => lose
[22875] => win
[22876] => win
[22877] => win
[22878] => win
[22879] => lose
[22880] => win
[22881] => lose
[22882] => win
[22883] => lose
[22884] => lose
[22885] => win
[22886] => win
[22887] => win
[22888] => lose
[22889] => lose
[22890] => lose
[22891] => lose
[22892] => lose
[22893] => win
[22894] => lose
[22895] => win
[22896] => win
[22897] => win
[22898] => lose
[22899] => win
[22900] => win
[22901] => win
[22902] => win
[22903] => win
[22904] => lose
[22905] => lose
[22906] => win
[22907] => win
[22908] => lose
[22909] => lose
[22910] => lose
[22911] => win
[22912] => win
[22913] => lose
[22914] => lose
[22915] => lose
[22916] => win
[22917] => lose
[22918] => lose
[22919] => win
[22920] => lose
[22921] => lose
[22922] => lose
[22923] => win
[22924] => lose
[22925] => lose
[22926] => lose
[22927] => win
[22928] => lose
[22929] => lose
[22930] => lose
[22931] => win
[22932] => win
[22933] => win
[22934] => lose
[22935] => lose
[22936] => lose
[22937] => lose
[22938] => win
[22939] => lose
[22940] => win
[22941] => win
[22942] => win
[22943] => win
[22944] => lose
[22945] => lose
[22946] => win
[22947] => win
[22948] => win
[22949] => lose
[22950] => win
[22951] => lose
[22952] => lose
[22953] => win
[22954] => lose
[22955] => win
[22956] => lose
[22957] => win
[22958] => win
[22959] => lose
[22960] => lose
[22961] => win
[22962] => lose
[22963] => win
[22964] => win
[22965] => lose
[22966] => lose
[22967] => lose
[22968] => win
[22969] => lose
[22970] => lose
[22971] => lose
[22972] => lose
[22973] => lose
[22974] => win
[22975] => win
[22976] => win
[22977] => lose
[22978] => lose
[22979] => win
[22980] => lose
[22981] => lose
[22982] => lose
[22983] => win
[22984] => lose
[22985] => win
[22986] => lose
[22987] => win
[22988] => lose
[22989] => win
[22990] => win
[22991] => lose
[22992] => lose
[22993] => win
[22994] => win
[22995] => win
[22996] => win
[22997] => lose
[22998] => win
[22999] => win
[23000] => win
[23001] => win
[23002] => win
[23003] => lose
[23004] => lose
[23005] => win
[23006] => win
[23007] => lose
[23008] => lose
[23009] => win
[23010] => lose
[23011] => lose
[23012] => win
[23013] => lose
[23014] => win
[23015] => lose
[23016] => lose
[23017] => lose
[23018] => lose
[23019] => win
[23020] => win
[23021] => win
[23022] => win
[23023] => win
[23024] => lose
[23025] => win
[23026] => win
[23027] => lose
[23028] => win
[23029] => lose
[23030] => win
[23031] => win
[23032] => lose
[23033] => win
[23034] => win
[23035] => win
[23036] => lose
[23037] => lose
[23038] => win
[23039] => lose
[23040] => lose
[23041] => win
[23042] => win
[23043] => win
[23044] => win
[23045] => win
[23046] => lose
[23047] => win
[23048] => win
[23049] => win
[23050] => win
[23051] => win
[23052] => win
[23053] => lose
[23054] => win
[23055] => win
[23056] => lose
[23057] => lose
[23058] => lose
[23059] => win
[23060] => win
[23061] => lose
[23062] => win
[23063] => win
[23064] => lose
[23065] => lose
[23066] => win
[23067] => win
[23068] => lose
[23069] => win
[23070] => win
[23071] => lose
[23072] => lose
[23073] => win
[23074] => win
[23075] => lose
[23076] => lose
[23077] => lose
[23078] => win
[23079] => win
[23080] => win
[23081] => win
[23082] => lose
[23083] => win
[23084] => lose
[23085] => lose
[23086] => win
[23087] => lose
[23088] => win
[23089] => lose
)
And this is the testing labels:
Array
(
[0] => lose
[1] => lose
[2] => win
[3] => win
[4] => lose
[5] => lose
[6] => lose
[7] => win
[8] => win
[9] => win
[10] => win
[11] => lose
[12] => win
[13] => lose
[14] => win
[15] => win
[16] => win
[17] => win
[18] => lose
[19] => lose
[20] => win
[21] => win
[22] => lose
[23] => lose
[24] => lose
[25] => lose
[26] => lose
[27] => win
[28] => win
[29] => lose
[30] => lose
[31] => win
[32] => win
[33] => win
[34] => win
[35] => win
[36] => win
[37] => win
[38] => win
[39] => lose
[40] => lose
[41] => lose
[42] => lose
[43] => lose
[44] => lose
[45] => win
[46] => lose
[47] => win
[48] => lose
[49] => win
[50] => win
[51] => win
[52] => lose
[53] => win
[54] => lose
[55] => win
[56] => lose
[57] => lose
[58] => win
[59] => lose
[60] => lose
[61] => win
[62] => win
[63] => lose
[64] => lose
[65] => lose
[66] => lose
[67] => win
[68] => win
[69] => lose
[70] => lose
[71] => win
[72] => win
[73] => win
[74] => win
[75] => lose
[76] => lose
[77] => lose
[78] => lose
[79] => lose
[80] => lose
[81] => lose
[82] => lose
[83] => lose
[84] => win
[85] => win
[86] => win
[87] => lose
[88] => lose
[89] => lose
[90] => lose
[91] => win
[92] => lose
[93] => lose
[94] => lose
[95] => lose
[96] => lose
[97] => win
[98] => lose
[99] => lose
[100] => lose
[101] => lose
[102] => lose
[103] => win
[104] => lose
[105] => lose
[106] => lose
[107] => win
[108] => win
[109] => lose
[110] => lose
[111] => win
[112] => lose
[113] => lose
[114] => win
[115] => win
[116] => lose
[117] => lose
[118] => lose
[119] => lose
[120] => win
[121] => win
[122] => win
[123] => lose
[124] => win
[125] => lose
[126] => win
[127] => lose
[128] => lose
[129] => win
[130] => win
[131] => win
[132] => win
[133] => win
[134] => win
[135] => lose
[136] => lose
[137] => win
[138] => lose
[139] => win
[140] => lose
[141] => lose
[142] => win
[143] => win
[144] => win
[145] => lose
[146] => win
[147] => lose
[148] => win
[149] => win
[150] => win
[151] => lose
[152] => lose
[153] => lose
[154] => win
[155] => win
[156] => win
[157] => win
[158] => win
[159] => win
[160] => lose
[161] => win
[162] => win
[163] => win
[164] => win
[165] => win
[166] => win
[167] => lose
[168] => win
[169] => win
[170] => lose
[171] => lose
[172] => lose
[173] => win
[174] => win
[175] => win
[176] => lose
[177] => lose
[178] => lose
[179] => win
[180] => win
[181] => lose
[182] => win
[183] => win
[184] => lose
[185] => win
[186] => win
[187] => win
[188] => lose
[189] => lose
[190] => lose
[191] => win
[192] => win
[193] => lose
[194] => win
[195] => win
[196] => win
[197] => win
[198] => win
[199] => win
[200] => win
[201] => lose
[202] => win
[203] => lose
[204] => win
[205] => lose
[206] => lose
[207] => lose
[208] => win
[209] => lose
[210] => lose
[211] => lose
[212] => lose
[213] => lose
[214] => lose
[215] => lose
[216] => win
[217] => win
[218] => win
[219] => win
[220] => lose
[221] => win
[222] => lose
[223] => lose
[224] => lose
[225] => lose
[226] => win
[227] => win
[228] => win
[229] => win
[230] => win
[231] => lose
[232] => win
[233] => win
[234] => lose
[235] => win
[236] => lose
[237] => win
[238] => lose
[239] => lose
[240] => lose
[241] => lose
[242] => lose
[243] => win
[244] => lose
[245] => lose
[246] => win
[247] => lose
[248] => lose
[249] => win
[250] => lose
[251] => win
[252] => win
[253] => lose
[254] => win
[255] => win
[256] => win
[257] => win
[258] => lose
[259] => win
[260] => win
[261] => lose
[262] => lose
[263] => win
[264] => lose
[265] => lose
[266] => win
[267] => lose
[268] => win
[269] => lose
[270] => win
[271] => win
[272] => win
[273] => lose
[274] => lose
[275] => win
[276] => win
[277] => win
[278] => win
[279] => lose
[280] => win
[281] => lose
[282] => lose
[283] => win
[284] => lose
[285] => lose
[286] => lose
[287] => lose
[288] => lose
[289] => win
[290] => lose
[291] => lose
[292] => win
[293] => lose
[294] => lose
[295] => lose
[296] => lose
[297] => win
[298] => lose
[299] => lose
[300] => lose
[301] => lose
[302] => lose
[303] => win
[304] => win
[305] => win
[306] => win
[307] => win
[308] => lose
[309] => lose
[310] => win
[311] => win
[312] => lose
[313] => win
[314] => lose
[315] => win
[316] => lose
[317] => lose
[318] => lose
[319] => win
[320] => lose
[321] => lose
[322] => lose
[323] => win
[324] => lose
[325] => lose
[326] => lose
[327] => lose
[328] => lose
[329] => lose
[330] => lose
[331] => win
[332] => win
[333] => win
[334] => win
[335] => lose
[336] => win
[337] => win
[338] => lose
[339] => lose
[340] => lose
[341] => win
[342] => lose
[343] => win
[344] => lose
[345] => lose
[346] => lose
[347] => lose
[348] => lose
[349] => lose
[350] => lose
[351] => lose
[352] => win
[353] => win
[354] => lose
[355] => win
[356] => win
[357] => win
[358] => lose
[359] => lose
[360] => win
[361] => win
[362] => lose
[363] => lose
[364] => win
[365] => win
[366] => win
[367] => win
[368] => win
[369] => win
[370] => win
[371] => win
[372] => win
[373] => lose
[374] => lose
[375] => win
[376] => lose
[377] => win
[378] => lose
[379] => lose
[380] => lose
[381] => win
[382] => lose
[383] => win
[384] => lose
[385] => lose
[386] => lose
[387] => lose
[388] => win
[389] => lose
[390] => lose
[391] => win
[392] => win
[393] => win
[394] => win
[395] => win
[396] => lose
[397] => win
[398] => lose
[399] => win
[400] => win
[401] => win
[402] => lose
[403] => lose
[404] => win
[405] => lose
[406] => win
[407] => win
[408] => win
[409] => lose
[410] => lose
[411] => win
[412] => win
[413] => win
[414] => lose
[415] => win
[416] => win
[417] => lose
[418] => lose
[419] => win
[420] => win
[421] => lose
[422] => lose
[423] => lose
[424] => lose
[425] => win
[426] => lose
[427] => win
[428] => lose
[429] => lose
[430] => win
[431] => lose
[432] => lose
[433] => lose
[434] => lose
[435] => win
[436] => win
[437] => win
[438] => win
[439] => win
[440] => win
[441] => win
[442] => win
[443] => lose
[444] => lose
[445] => lose
[446] => lose
[447] => lose
[448] => win
[449] => lose
[450] => win
[451] => win
[452] => lose
[453] => win
[454] => win
[455] => win
[456] => win
[457] => win
[458] => win
[459] => lose
[460] => lose
[461] => win
[462] => lose
[463] => lose
[464] => lose
[465] => win
[466] => lose
[467] => win
[468] => win
[469] => win
[470] => lose
[471] => lose
[472] => win
[473] => win
[474] => lose
[475] => win
[476] => lose
[477] => lose
[478] => lose
[479] => win
[480] => lose
[481] => lose
[482] => lose
[483] => win
[484] => lose
[485] => win
[486] => win
[487] => lose
[488] => win
[489] => win
[490] => win
[491] => win
[492] => win
[493] => win
[494] => lose
[495] => win
[496] => win
[497] => lose
[498] => lose
[499] => lose
[500] => win
[501] => lose
[502] => win
[503] => win
[504] => lose
[505] => lose
[506] => lose
[507] => lose
[508] => lose
[509] => lose
[510] => win
[511] => lose
[512] => lose
[513] => lose
[514] => win
[515] => win
[516] => win
[517] => lose
[518] => win
[519] => lose
[520] => lose
[521] => lose
[522] => win
[523] => win
[524] => win
[525] => lose
[526] => win
[527] => lose
[528] => win
[529] => win
[530] => lose
[531] => lose
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[4675] => win
[4676] => lose
[4677] => win
[4678] => lose
[4679] => win
[4680] => win
[4681] => lose
[4682] => lose
[4683] => win
[4684] => lose
[4685] => lose
[4686] => win
[4687] => lose
[4688] => win
[4689] => lose
[4690] => win
[4691] => win
[4692] => win
[4693] => lose
[4694] => win
[4695] => win
[4696] => win
[4697] => lose
[4698] => win
[4699] => win
[4700] => lose
[4701] => win
[4702] => win
[4703] => win
[4704] => win
[4705] => lose
[4706] => lose
[4707] => win
[4708] => win
[4709] => lose
[4710] => lose
[4711] => lose
[4712] => lose
[4713] => lose
[4714] => lose
[4715] => win
[4716] => win
[4717] => win
[4718] => win
[4719] => lose
[4720] => win
[4721] => win
[4722] => lose
[4723] => lose
[4724] => lose
[4725] => win
[4726] => lose
[4727] => lose
[4728] => win
[4729] => lose
[4730] => win
[4731] => win
[4732] => lose
[4733] => lose
[4734] => lose
[4735] => lose
[4736] => lose
[4737] => win
[4738] => win
[4739] => lose
[4740] => lose
[4741] => lose
[4742] => lose
[4743] => win
[4744] => lose
[4745] => lose
[4746] => lose
[4747] => lose
[4748] => win
[4749] => lose
[4750] => win
[4751] => lose
[4752] => lose
[4753] => lose
[4754] => win
[4755] => win
[4756] => lose
[4757] => win
[4758] => win
[4759] => win
[4760] => win
[4761] => win
[4762] => lose
[4763] => lose
[4764] => lose
[4765] => lose
[4766] => win
[4767] => win
[4768] => lose
[4769] => win
[4770] => win
[4771] => win
[4772] => win
[4773] => win
[4774] => win
[4775] => lose
[4776] => lose
[4777] => lose
[4778] => win
[4779] => win
[4780] => lose
[4781] => lose
[4782] => lose
[4783] => win
[4784] => win
[4785] => lose
[4786] => lose
[4787] => win
[4788] => win
[4789] => win
[4790] => lose
[4791] => lose
[4792] => lose
[4793] => win
[4794] => lose
[4795] => win
[4796] => win
[4797] => win
[4798] => win
[4799] => win
[4800] => win
[4801] => win
[4802] => lose
[4803] => win
[4804] => win
[4805] => win
[4806] => win
[4807] => win
[4808] => lose
[4809] => win
[4810] => lose
[4811] => lose
[4812] => lose
[4813] => lose
[4814] => win
[4815] => win
[4816] => lose
[4817] => win
[4818] => win
[4819] => lose
[4820] => win
[4821] => lose
[4822] => win
[4823] => win
[4824] => win
[4825] => win
[4826] => lose
[4827] => lose
[4828] => win
[4829] => lose
[4830] => win
[4831] => win
[4832] => lose
[4833] => lose
[4834] => lose
[4835] => lose
[4836] => lose
[4837] => win
[4838] => lose
[4839] => win
[4840] => win
[4841] => lose
[4842] => lose
[4843] => win
[4844] => lose
[4845] => win
[4846] => win
[4847] => win
[4848] => win
[4849] => lose
[4850] => win
[4851] => lose
[4852] => win
[4853] => win
[4854] => win
[4855] => win
[4856] => win
[4857] => win
[4858] => lose
[4859] => lose
[4860] => win
[4861] => lose
[4862] => lose
[4863] => lose
[4864] => lose
[4865] => lose
[4866] => lose
[4867] => win
[4868] => lose
[4869] => win
[4870] => lose
[4871] => lose
[4872] => win
[4873] => lose
[4874] => win
[4875] => win
[4876] => lose
[4877] => win
[4878] => lose
[4879] => win
[4880] => lose
[4881] => win
[4882] => win
[4883] => lose
[4884] => lose
[4885] => lose
[4886] => win
[4887] => lose
[4888] => lose
[4889] => win
[4890] => lose
[4891] => lose
[4892] => lose
[4893] => win
[4894] => lose
[4895] => win
[4896] => win
[4897] => lose
[4898] => lose
[4899] => lose
[4900] => lose
[4901] => lose
[4902] => win
[4903] => lose
[4904] => win
[4905] => win
[4906] => win
[4907] => lose
[4908] => win
[4909] => lose
[4910] => lose
[4911] => lose
[4912] => win
[4913] => win
[4914] => win
[4915] => lose
[4916] => win
[4917] => lose
[4918] => win
[4919] => lose
[4920] => win
[4921] => win
[4922] => lose
[4923] => win
[4924] => lose
[4925] => win
[4926] => win
[4927] => lose
[4928] => win
[4929] => lose
[4930] => win
[4931] => win
[4932] => win
[4933] => lose
[4934] => win
[4935] => lose
[4936] => lose
[4937] => lose
[4938] => lose
[4939] => win
[4940] => lose
[4941] => lose
[4942] => win
[4943] => win
[4944] => win
[4945] => win
[4946] => lose
[4947] => lose
[4948] => lose
[4949] => lose
[4950] => win
[4951] => lose
[4952] => lose
[4953] => lose
[4954] => win
[4955] => lose
[4956] => lose
[4957] => lose
[4958] => lose
[4959] => lose
[4960] => win
[4961] => win
[4962] => win
[4963] => win
[4964] => win
[4965] => lose
[4966] => lose
[4967] => lose
[4968] => lose
[4969] => lose
[4970] => lose
[4971] => win
[4972] => lose
[4973] => lose
[4974] => lose
[4975] => lose
[4976] => lose
[4977] => lose
[4978] => lose
[4979] => win
[4980] => win
[4981] => win
[4982] => lose
[4983] => lose
[4984] => lose
[4985] => lose
[4986] => lose
[4987] => win
[4988] => win
[4989] => lose
[4990] => win
[4991] => lose
[4992] => win
[4993] => lose
[4994] => lose
[4995] => win
[4996] => lose
[4997] => lose
[4998] => win
[4999] => lose
[5000] => win
[5001] => lose
[5002] => lose
[5003] => win
[5004] => lose
[5005] => win
[5006] => win
[5007] => win
[5008] => lose
[5009] => win
[5010] => lose
[5011] => win
[5012] => win
[5013] => lose
[5014] => win
[5015] => lose
[5016] => win
[5017] => win
[5018] => win
[5019] => win
[5020] => win
[5021] => win
[5022] => lose
[5023] => lose
[5024] => lose
[5025] => lose
[5026] => lose
[5027] => win
[5028] => win
[5029] => lose
[5030] => lose
[5031] => lose
[5032] => lose
[5033] => win
[5034] => lose
[5035] => lose
[5036] => win
[5037] => lose
[5038] => win
[5039] => win
[5040] => win
[5041] => win
[5042] => lose
[5043] => lose
[5044] => lose
[5045] => lose
[5046] => lose
[5047] => win
[5048] => lose
[5049] => lose
[5050] => lose
[5051] => lose
[5052] => lose
[5053] => lose
[5054] => lose
[5055] => win
[5056] => win
[5057] => win
[5058] => win
[5059] => win
[5060] => lose
[5061] => win
[5062] => lose
[5063] => lose
[5064] => lose
[5065] => lose
[5066] => lose
[5067] => lose
[5068] => lose
[5069] => win
[5070] => lose
[5071] => win
[5072] => win
[5073] => win
[5074] => win
[5075] => lose
[5076] => win
[5077] => lose
[5078] => lose
[5079] => lose
[5080] => lose
[5081] => win
[5082] => win
[5083] => win
[5084] => lose
[5085] => win
[5086] => win
[5087] => win
[5088] => win
[5089] => lose
[5090] => win
[5091] => win
[5092] => lose
[5093] => lose
[5094] => win
[5095] => win
[5096] => lose
[5097] => win
[5098] => lose
[5099] => lose
[5100] => lose
[5101] => lose
[5102] => lose
[5103] => lose
[5104] => win
[5105] => win
[5106] => lose
[5107] => win
[5108] => lose
[5109] => win
[5110] => lose
[5111] => lose
[5112] => lose
[5113] => win
[5114] => win
[5115] => lose
[5116] => win
[5117] => lose
[5118] => win
[5119] => lose
[5120] => win
[5121] => win
[5122] => win
[5123] => win
[5124] => win
[5125] => win
[5126] => lose
[5127] => win
[5128] => win
[5129] => win
[5130] => win
[5131] => win
[5132] => lose
[5133] => lose
[5134] => lose
[5135] => lose
[5136] => lose
[5137] => win
[5138] => win
[5139] => win
[5140] => lose
[5141] => win
[5142] => win
[5143] => win
[5144] => win
[5145] => win
[5146] => win
[5147] => lose
[5148] => win
[5149] => win
[5150] => win
[5151] => win
[5152] => win
[5153] => win
[5154] => win
[5155] => win
[5156] => win
[5157] => lose
)
I'm pretty sure those blank predictions are the cause of the "new class" showing up in the report. I'll have to dig deeper as to why that occurs exactly but it's related to unstable training most likely caused by the high variance among the features.
Did standardizing the features help at all?