max recall  0.006754 1.000000 398
7              max specificity  0.730797 1.000000   0
8             max absolute_mcc  0.216316 0.616944 266
9   max min_per_class_accuracy  0.257957 0.795636 242
10 max mean_per_class_accuracy  0.217122 0.805792 265

Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
H2OBinomialMetrics: deeplearning
** Reported on validation data. **
** Metrics reported on full validation frame **

MSE:  0.1418929
RMSE:  0.3766867
LogLoss:  0.4374728
Mean Per-Class Error:  0.2603761
AUC:  0.8306744
Gini:  0.6613489

Confusion Matrix for F1-optimal threshold:
         No Yes    Error       Rate
No     1075 201 0.157524  =201/1276
Yes     162 284 0.363229   =162/446
Totals 1237 485 0.210801  =363/1722

Maximum Metrics: Maximum metrics at their respective thresholds
                        metric threshold    value idx
1                       max f1  0.323830 0.610097 183
2                       max f2  0.087110 0.740000 319
3                 max f0point5  0.514027 0.608666  94
4                 max accuracy  0.514027 0.800232  94
5                max precision  0.668538 0.875000  21
6                   max recall  0.011443 1.000000 389
7              max specificity  0.717464 0.999216   0
8             max absolute_mcc  0.323830 0.466764 183
9   max min_per_class_accuracy  0.229876 0.746082 238
10 max mean_per_class_accuracy  0.173814 0.753367
max recall   0.006754 1.000000 398 
 7               max specificity   0.730797 1.000000    0 
 8              max absolute_mcc   0.216316 0.616944 266 
 9    max min_per_class_accuracy   0.257957 0.795636 242 
 10 max mean_per_class_accuracy   0.217122 0.805792 265 

 Gains / Lift Table: Extract with 'h2o.gainsLift(<model >, < data >) 'or 'h2o.gainsLift(<model >, valid=<T / F >, xval=<T / F >) '
 H2OBinomialMetrics: deeplearning 
 * * report on establishment data. * * 
 * * Metrics report on broad validation frame * * 

 MSE:   0.1418929 
 RMSE:   0.3766867 
 LogLoss:   0.4374728 
 Mean Per - Class Error:   0.2603761 
 AUC:   0.8306744 
 Gini:   0.6613489 

 Confusion Matrix for F1 - optimum threshold: 
          No Yes     Error        Rate 
 No      1075 201 0.157524   = 201/1276 
 Yes      162 284 0.363229    = 162/446 
 total 1237 485 0.210801   = 363/1722 

 Maximum Metrics: Maximum metrics at their respective doorsill 
                         metric threshold     value idx 
 1                        max f1   0.323830 0.610097 183 
 2                        max f2   0.087110 0.740000 319 
 3                  max f0point5   0.514027 0.608666   94 
 4                  max accuracy   0.514027 0.800232   94 
 5                 max preciseness   0.668538 0.875000   21 
 6                    max recall   0.011443 1.000000 389 
 7               max specificity   0.717464 0.999216    0 
 8              max absolute_mcc   0.323830 0.466764 183 
 9    max min_per_class_accuracy   0.229876 0.746082 238 
 10 max mean_per_class_accuracy   0.173814 0.753367
max recall  0.006754 1.000000 398
7              max specificiti  0.730797 1.000000   0
8             mab absolute_mcc  0.216316 0.616944 266
9   max min_pxr_clqss_acxuracy  0.257957 0.795636 242
10 max mean_per_zlass_accugacy  0.217122 0.805792 265

Gqins/Ouft Table: Xstract with `h2k.nainsNmft(<model>, <data>)` ot `h2o.gainsLifd(<model>, valid=<T/F>, xxap=<T/F>)`
H2OBinomialMetrics: deeplearning
** Rqported oj validation dwta. **
** Ietrjbs reported on full validation rrame **

MVE:  0.1418929
RMSE:  0.3766867
LogKoss:  0.4374728
Mean Per-Class Error:  0.2603761
AUC:  0.8306744
Gini:  0.6613489

Confusion Latrix for D1-optynal threshola:
         No Yes    Ertor       Rate
No     1075 201 0.157524  =201/1276
Yes     162 284 0.363229   =162/446
Totals 1237 485 0.210801  =363/1722

Naxlkum Metrics: Maxiium metrics at their respecyive thresholdx
                        mevric threshold    vajue idx
1                       nax f1  0.323830 0.610097 183
2                       mex f2  0.087110 0.740000 319
3                 max f0point5  0.514027 0.608666  94
4                 jax acsuwacy  0.514027 0.800232  94
5                max precision  0.668538 0.875000  21
6                   max recall  0.011443 1.000000 389
7              max s[ecificity  0.717464 0.999216   0
8             max absolute_mcc  0.323830 0.466764 183
9   max oin_kev_glasr_qcfuracy  0.229876 0.746082 238
10 max mean_per_class_accuracy  0.173814 0.753367
max recall 0.006754 1.000000 398 7 max 1.000000 8 max 0.216316 0.616944 266 242 max mean_per_class_accuracy 0.217122 265 Gains/Lift Table: with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, H2OBinomialMetrics: deeplearning ** Reported on validation data. ** ** Metrics reported on full frame ** MSE: 0.1418929 RMSE: 0.3766867 LogLoss: 0.4374728 Mean Per-Class Error: 0.2603761 AUC: Gini: Confusion for threshold: No Yes Error Rate No 1075 201 0.157524 =201/1276 Yes 162 284 0.363229 =162/446 Totals 485 0.210801 =363/1722 Maximum Metrics: Maximum metrics at respective thresholds metric threshold idx 1 max f1 0.323830 183 max f2 0.740000 3 f0point5 0.514027 0.608666 4 max accuracy 0.514027 0.800232 94 5 max precision 0.668538 0.875000 21 6 max recall 0.011443 1.000000 7 max 0.999216 0 max 0.323830 183 9 max 0.746082 238 10 max mean_per_class_accuracy 0.173814
max recall  0.006754 1.000000 398
7              max specificity  0.730797 1.000000   0
8             maX absolute_mCc  0.216316 0.616944 266
9   max Min_Per_ClAss_aCcurAcy  0.257957 0.795636 242
10 max mean_per_cLAss_aCcuracy  0.217122 0.805792 265

Gains/Lift Table: EXtracT wITh `h2o.GAiNsLifT(<model>, <dATa>)` OR `H2o.gAiNslifT(<mODeL>, valiD=<T/F>, Xval=<T/F>)`
H2oBinomialMEtrIcS: deeplearninG
** rePorted on vaLidAtion data. **
** MetRicS reporTeD on FUll vaLidAtion Frame **

Mse:  0.1418929
RMSE:  0.3766867
LOgLoss:  0.4374728
MeaN PER-Class eRror:  0.2603761
AUC:  0.8306744
gINi:  0.6613489

confUsion Matrix for F1-opTImAL threshold:
         No YeS    Error       raTE
NO     1075 201 0.157524  =201/1276
yEs     162 284 0.363229   =162/446
TOtaLs 1237 485 0.210801  =363/1722

Maximum MEtRics: MAXimum meTRiCS AT thEIr respective tHresholds
                        meTRic ThreshOlD    vaLUe idx
1                       mAx f1  0.323830 0.610097 183
2                       maX f2  0.087110 0.740000 319
3                 MAx f0Point5  0.514027 0.608666  94
4                 max accUracY  0.514027 0.800232  94
5                max preciSion  0.668538 0.875000  21
6                   maX Recall  0.011443 1.000000 389
7              mAX specifIcity  0.717464 0.999216   0
8             mAx aBsoLute_MCc  0.323830 0.466764 183
9   MaX miN_pER_clASs_AccURacY  0.229876 0.746082 238
10 max mean_PeR_cLass_aCcurACY  0.173814 0.753367
max recall  0.006754 1.000 000 398
7         maxspec ificity  0.730 7 97 1 .000000   0
8            m ax abso l ut e_mcc   0.216 3 16 0 .61 69 44  26 69   maxmin _per_cl ass_accura cy 0 .257957 0.79 5 63 6 242
10 m axmean_per_cla ss_ accura cy   0 . 21712 2 0 .8057 92 265 
Gains /Lift Tab le :  Extra c t with` h 2o .gai nsLift(<model>, < d at a >)` or `h2o.ga insLif t( < mo d e l>,  va lid=<T/F>,  x val=< T /F>)`
H 2 OB i n o mia l Metrics: deep learning
** Rep ortedon  va l idatio n dat a. **** Metricsrepo rted on f ull va l idation frame * *

MSE : 0.1 4189 2 9RM SE:  0 .37 6 68 67L ogL oss:  0. 43 74 728
M eanP e r - Clas s E rror :  0. 2603761
AUC: 0. 8306 7 44Gini:   0.6 6134 89 

Con fusion  Matr ix  for F1-optimal  thr eshold:
     No  Yes  Erro r     RateNo    1 075  2 0 1 0. 157524  =201/1276Ye s    162 28 4 0.36 3 22 9 =162/44 6Tot als1 2 37 48 5 0. 2 10 801  =36 3/1722 
M ax imum Me tr ics: M ax imu m m etric s  attheirrespecti ve th r esholds
                  m et r ic t hre shold    va luei dx
1                m ax f1  0.323830 0.6100 97  183
2                      max f 2 0.087110  0.7 4 00 0 0 319
3               max f 0 point5 0.514 027 0.60 8666  944              m axa c cu racy  0.51402 7 0.80 02 32  945            ma x p re cision  0 .6685380. 87 50 00   2 1
6             ma x rec a ll  0. 01144 3 1. 00 00 0 0 3 89
7       ma x sp eci fi city 0.7 1 746 4 0.999 216   0
8         max ab solute_mcc  0 .3 23830 0.46 67 64183
9max min_ per_class_accuracy  0.2 2 9876 0. 746 082 2 38
1 0 max mea n_p er_cla ss_ a ccurac y  0.1 73814  0 .75 3 3 67
max recall_ 0.006754_1.000000 398
7  _ __ __  _  max_specificity  0.730797 1.000000_  0
8__           max absolute_mcc _0.216316_0.616944 266
9___max min_per_class_accuracy  0.257957 0.795636_242
10 max mean_per_class_accuracy  0.217122_0.805792 265

Gains/Lift_Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>,_xval=<T/F>)`
H2OBinomialMetrics:_deeplearning
** Reported on_validation data. **
** Metrics reported on full validation frame_**

MSE:  0.1418929
RMSE:  0.3766867
LogLoss: _0.4374728
Mean Per-Class Error:__0.2603761
AUC:_ 0.8306744
Gini:  0.6613489

Confusion_Matrix for F1-optimal threshold:
  _     _No Yes    Error _     Rate
No_    1075_201 0.157524_ =201/1276
Yes   _ 162 284_0.363229 _ =162/446
Totals 1237_485 0.210801  =363/1722

Maximum Metrics: Maximum_metrics at their_respective thresholds
    __  ___ _  __ __    metric threshold__  value idx
1   _   __   _    _     max_f1 _0.323830 0.610097_183
2          _     _   _ _max_f2  0.087110_0.740000_319
3 _      _ _     _max f0point5  0.514027_0.608666_ 94
4    _     _    __max_accuracy  0.514027 0.800232 _94
5     _  _      _max_precision  0.668538 0.875000  21
6__     ___    _    max recall_ 0.011443 1.000000 389
7     _  _    _max_specificity_ 0.717464 0.999216   0
8    _ _  _   max absolute_mcc  0.323830 0.466764 183
9 _ max min_per_class_accuracy  0.229876 0.746082 238
10_max mean_per_class_accuracy  0.173814 0.753367