To this end, we’ll use a dataset donated to the UCI Machine Learning Data Repository (http://archive.ics.uci.edu/ml) by W. Frey and D. J. Slate. The dataset contains 20,000 examples of 26 English alphabet capital letters as printed using 20 different randomly reshaped and distorted black and white fonts.
letters <- read.csv("http://www.sci.csueastbay.edu/~esuess/classes/Statistics_6620/Presentations/ml11/letterdata.csv")
str(letters)'data.frame': 20000 obs. of 17 variables:
$ letter: Factor w/ 26 levels "A","B","C","D",..: 20 9 4 14 7 19 2 1 10 13 ...
$ xbox : int 2 5 4 7 2 4 4 1 2 11 ...
$ ybox : int 8 12 11 11 1 11 2 1 2 15 ...
$ width : int 3 3 6 6 3 5 5 3 4 13 ...
$ height: int 5 7 8 6 1 8 4 2 4 9 ...
$ onpix : int 1 2 6 3 1 3 4 1 2 7 ...
$ xbar : int 8 10 10 5 8 8 8 8 10 13 ...
$ ybar : int 13 5 6 9 6 8 7 2 6 2 ...
$ x2bar : int 0 5 2 4 6 6 6 2 2 6 ...
$ y2bar : int 6 4 6 6 6 9 6 2 6 2 ...
$ xybar : int 6 13 10 4 6 5 7 8 12 12 ...
$ x2ybar: int 10 3 3 4 5 6 6 2 4 1 ...
$ xy2bar: int 8 9 7 10 9 6 6 8 8 9 ...
$ xedge : int 0 2 3 6 1 0 2 1 1 8 ...
$ xedgey: int 8 8 7 10 7 8 8 6 6 1 ...
$ yedge : int 0 4 3 2 5 9 7 2 1 1 ...
$ yedgex: int 8 10 9 8 10 7 10 7 7 8 ...letters_train <- letters[1:16000, ]
letters_test <- letters[16001:20000, ]library(kernlab)
Attaching package: ‘kernlab’
The following object is masked from ‘package:ggplot2’:
alpha
The following object is masked from ‘package:psych’:
alphaletter_classifier <- ksvm(letter ~ ., data = letters_train,
kernel = "vanilladot") Setting default kernel parameters letter_classifierSupport Vector Machine object of class "ksvm"
SV type: C-svc (classification)
parameter : cost C = 1
Linear (vanilla) kernel function.
Number of Support Vectors : 7037
Objective Function Value : -14.1746 -20.0072 -23.5628 -6.2009 -7.5524 -32.7694 -49.9786 -18.1824 -62.1111 -32.7284 -16.2209 -32.2837 -28.9777 -51.2195 -13.276 -35.6217 -30.8612 -16.5256 -14.6811 -32.7475 -30.3219 -7.7956 -11.8138 -32.3463 -13.1262 -9.2692 -153.1654 -52.9678 -76.7744 -119.2067 -165.4437 -54.6237 -41.9809 -67.2688 -25.1959 -27.6371 -26.4102 -35.5583 -41.2597 -122.164 -187.9178 -222.0856 -21.4765 -10.3752 -56.3684 -12.2277 -49.4899 -9.3372 -19.2092 -11.1776 -100.2186 -29.1397 -238.0516 -77.1985 -8.3339 -4.5308 -139.8534 -80.8854 -20.3642 -13.0245 -82.5151 -14.5032 -26.7509 -18.5713 -23.9511 -27.3034 -53.2731 -11.4773 -5.12 -13.9504 -4.4982 -3.5755 -8.4914 -40.9716 -49.8182 -190.0269 -43.8594 -44.8667 -45.2596 -13.5561 -17.7664 -87.4105 -107.1056 -37.0245 -30.7133 -112.3218 -32.9619 -27.2971 -35.5836 -17.8586 -5.1391 -43.4094 -7.7843 -16.6785 -58.5103 -159.9936 -49.0782 -37.8426 -32.8002 -74.5249 -133.3423 -11.1638 -5.3575 -12.438 -30.9907 -141.6924 -54.2953 -179.0114 -99.8896 -10.288 -15.1553 -3.7815 -67.6123 -7.696 -88.9304 -47.6448 -94.3718 -70.2733 -71.5057 -21.7854 -12.7657 -7.4383 -23.502 -13.1055 -239.9708 -30.4193 -25.2113 -136.2795 -140.9565 -9.8122 -34.4584 -6.3039 -60.8421 -66.5793 -27.2816 -214.3225 -34.7796 -16.7631 -135.7821 -160.6279 -45.2949 -25.1023 -144.9059 -82.2352 -327.7154 -142.0613 -158.8821 -32.2181 -32.8887 -52.9641 -25.4937 -47.9936 -6.8991 -9.7293 -36.436 -70.3907 -187.7611 -46.9371 -89.8103 -143.4213 -624.3645 -119.2204 -145.4435 -327.7748 -33.3255 -64.0607 -145.4831 -116.5903 -36.2977 -66.3762 -44.8248 -7.5088 -217.9246 -12.9699 -30.504 -2.0369 -6.126 -14.4448 -21.6337 -57.3084 -20.6915 -184.3625 -20.1052 -4.1484 -4.5344 -0.828 -121.4411 -7.9486 -58.5604 -21.4878 -13.5476 -5.646 -15.629 -28.9576 -20.5959 -76.7111 -27.0119 -94.7101 -15.1713 -10.0222 -7.6394 -1.5784 -87.6952 -6.2239 -99.3711 -101.0906 -45.6639 -24.0725 -61.7702 -24.1583 -52.2368 -234.3264 -39.9749 -48.8556 -34.1464 -20.9664 -11.4525 -123.0277 -6.4903 -5.1865 -8.8016 -9.4618 -21.7742 -24.2361 -123.3984 -31.4404 -88.3901 -30.0924 -13.8198 -9.2701 -3.0823 -87.9624 -6.3845 -13.968 -65.0702 -105.523 -13.7403 -13.7625 -50.4223 -2.933 -8.4289 -80.3381 -36.4147 -112.7485 -4.1711 -7.8989 -1.2676 -90.8037 -21.4919 -7.2235 -47.9557 -3.383 -20.433 -64.6138 -45.5781 -56.1309 -6.1345 -18.6307 -2.374 -72.2553 -111.1885 -106.7664 -23.1323 -19.3765 -54.9819 -34.2953 -64.4756 -20.4115 -6.689 -4.378 -59.141 -34.2468 -58.1509 -33.8665 -10.6902 -53.1387 -13.7478 -20.1987 -55.0923 -3.8058 -60.0382 -235.4841 -12.6837 -11.7407 -17.3058 -9.7167 -65.8498 -17.1051 -42.8131 -53.1054 -25.0437 -15.302 -44.0749 -16.9582 -62.9773 -5.204 -5.2963 -86.1704 -3.7209 -6.3445 -1.1264 -122.5771 -23.9041 -355.0145 -31.1013 -32.619 -4.9664 -84.1048 -134.5957 -72.8371 -23.9002 -35.3077 -11.7119 -22.2889 -1.8598 -59.2174 -8.8994 -150.742 -1.8533 -1.9711 -9.9676 -0.5207 -26.9229 -30.429 -5.6289
Training error : 0.130062 This information tells us very little about how well the model will perform in the real world. We’ll need to examine its performance on the testing dataset to know whether it generalizes well to unseen data.
letter_predictions <- predict(letter_classifier, letters_test)
head(letter_predictions)[1] U N V X N H
Levels: A B C D E F G H I J K L M N O P Q R S T U V W X Y Ztable(letters_test$letter, letter_predictions) letter_predictions
A B C D E F G H I J K L M N O P Q R S T U
A 144 0 0 2 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1
B 0 121 0 2 0 0 1 0 1 1 1 0 0 0 0 0 0 7 1 0 0
C 0 0 120 0 5 0 2 0 0 0 9 0 1 0 2 0 0 0 0 0 3
D 0 5 0 156 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1
E 0 2 4 0 127 0 9 0 0 0 0 2 0 0 0 0 0 1 1 3 0
F 0 0 0 1 3 138 2 1 1 1 0 0 0 1 0 2 0 0 0 2 0
G 0 1 10 3 1 2 123 0 0 0 2 1 1 0 1 1 8 3 3 0 0
H 0 2 2 10 1 2 2 102 0 2 5 1 1 1 2 0 2 8 0 0 2
I 0 0 2 4 0 6 0 0 141 5 0 0 0 0 0 0 0 0 1 0 0
J 1 0 0 3 0 0 0 2 8 128 0 0 0 0 1 0 0 0 1 0 0
K 0 1 1 4 3 0 1 3 0 0 118 0 0 0 0 0 0 13 0 1 0
L 0 0 3 3 4 0 2 2 0 0 0 133 0 0 0 0 3 0 1 0 0
M 1 1 0 0 0 0 1 3 0 0 0 0 135 0 0 0 0 0 0 0 0
N 2 0 0 5 0 0 0 4 0 0 2 0 4 145 1 0 0 1 0 0 0
O 2 0 2 5 0 0 1 20 0 1 0 0 0 0 99 2 3 1 0 0 1
P 0 2 0 3 0 16 2 0 1 1 1 0 0 0 3 130 1 1 0 0 0
Q 5 2 0 1 2 0 8 2 0 3 0 1 0 0 3 0 124 0 14 0 0
R 0 3 0 4 0 0 2 3 0 0 7 0 0 3 0 0 0 138 0 0 0
S 1 5 0 0 10 3 4 0 3 2 0 5 0 0 0 0 5 0 101 3 0
T 1 0 0 0 0 0 3 3 0 0 1 0 0 0 0 0 0 1 3 133 0
U 1 0 0 0 0 0 0 0 0 0 3 0 3 1 3 0 0 0 0 1 152
V 0 2 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0
W 1 0 0 0 0 0 0 0 0 0 0 0 8 2 0 0 0 0 0 0 0
X 0 1 0 3 2 1 1 0 5 1 5 0 0 0 0 0 0 0 2 0 1
Y 0 0 0 3 0 2 0 1 1 0 0 0 0 0 0 1 2 0 0 2 1
Z 1 0 0 1 3 0 0 0 1 6 0 1 0 0 0 0 0 0 10 2 0
letter_predictions
V W X Y Z
A 0 0 0 3 2
B 0 0 1 0 0
C 0 0 0 0 0
D 0 0 0 0 0
E 0 0 2 0 1
F 1 0 0 0 0
G 3 1 0 0 0
H 4 0 1 1 0
I 0 0 3 0 3
J 0 0 0 0 4
K 0 0 1 0 0
L 0 0 6 0 0
M 1 2 0 0 0
N 2 0 0 0 0
O 1 0 1 0 0
P 0 0 0 7 0
Q 3 0 0 0 0
R 1 0 0 0 0
S 0 0 1 0 18
T 0 0 0 3 3
U 0 4 0 0 0
V 126 4 0 0 0
W 1 127 0 0 0
X 0 0 137 0 0
Y 4 0 1 127 0
Z 0 0 1 0 132agreement <- letter_predictions == letters_test$letter
table(agreement)agreement
FALSE TRUE
643 3357 prop.table(table(agreement))agreement
FALSE TRUE
0.16075 0.83925 set.seed(12345)
letter_classifier_rbf <- ksvm(letter ~ ., data = letters_train, kernel = "rbfdot")
letter_predictions_rbf <- predict(letter_classifier_rbf, letters_test)
table(letters_test$letter, letter_predictions_rbf) letter_predictions_rbf
A B C D E F G H I J K L M N O P Q R S T U
A 151 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
B 0 128 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 3 2 0 0
C 0 0 133 0 2 0 2 0 0 0 1 0 0 0 2 0 0 1 0 0 1
D 0 3 0 161 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1
E 0 0 3 0 137 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0
F 0 1 0 0 2 148 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0
G 0 0 1 2 0 0 154 2 0 0 0 1 1 0 0 0 0 2 0 0 0
H 0 2 0 8 0 0 2 126 0 0 4 0 1 0 0 0 1 5 0 0 1
I 0 0 2 2 0 3 0 0 151 3 0 0 0 0 0 1 0 0 1 0 0
J 0 0 0 3 1 0 0 1 3 136 0 0 0 0 1 0 0 0 2 0 0
K 0 0 0 1 0 0 0 2 0 0 132 0 0 0 0 0 0 9 0 0 0
L 0 1 1 0 4 0 2 1 0 0 0 142 0 0 0 0 0 1 1 0 0
M 0 2 0 0 0 0 2 1 0 0 0 0 138 0 0 0 0 0 0 0 0
N 0 1 0 1 0 0 0 3 0 0 1 0 1 150 5 0 0 3 0 0 0
O 0 0 0 1 0 0 2 0 0 0 0 0 0 0 129 0 3 2 0 0 0
P 0 2 0 3 1 11 1 1 0 0 0 0 0 0 2 141 3 1 0 0 0
Q 3 1 0 1 0 0 0 1 0 0 0 0 0 0 4 0 158 0 0 0 0
R 0 3 0 3 0 0 0 0 0 0 3 0 0 2 0 0 0 150 0 0 0
S 0 3 0 0 2 1 0 0 0 0 0 1 0 0 0 0 0 0 152 0 0
T 1 0 0 2 1 0 2 2 0 0 0 0 0 0 0 0 0 1 0 140 0
U 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 161
V 0 3 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
W 0 1 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0
X 0 1 0 2 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0
Y 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
Z 0 0 0 0 2 0 0 0 0 3 0 0 0 0 0 0 0 0 2 0 0
letter_predictions_rbf
V W X Y Z
A 0 0 0 4 0
B 0 0 1 0 0
C 0 0 0 0 0
D 0 0 0 0 0
E 0 0 1 0 3
F 0 0 0 0 0
G 0 1 0 0 0
H 0 0 0 1 0
I 0 0 0 0 2
J 0 0 0 0 1
K 0 0 2 0 0
L 0 0 4 0 0
M 0 1 0 0 0
N 1 0 0 0 0
O 0 2 0 0 0
P 0 0 0 2 0
Q 0 0 0 0 0
R 0 0 0 0 0
S 0 0 1 0 1
T 0 0 1 1 0
U 2 3 0 0 0
V 131 0 0 0 0
W 0 135 0 0 0
X 0 0 153 0 0
Y 1 0 1 138 0
Z 0 0 1 0 150agreement_rbf <- letter_predictions_rbf == letters_test$letter
table(agreement_rbf)agreement_rbf
FALSE TRUE
275 3725 prop.table(table(agreement_rbf))agreement_rbf
FALSE TRUE
0.06875 0.93125 library(h2o)package ‘h2o’ was built under R version 3.3.2
----------------------------------------------------------------------
Your next step is to start H2O:
> h2o.init()
For H2O package documentation, ask for help:
> ??h2o
After starting H2O, you can use the Web UI at http://localhost:54321
For more information visit http://docs.h2o.ai
----------------------------------------------------------------------
Attaching package: ‘h2o’
The following objects are masked from ‘package:stats’:
cor, sd, var
The following objects are masked from ‘package:base’:
&&, %*%, %in%, ||, apply, as.factor, as.numeric, colnames, colnames<-,
ifelse, is.character, is.factor, is.numeric, log, log10, log1p, log2, round,
signif, trunch2o.init()
H2O is not running yet, starting it now...
Note: In case of errors look at the following log files:
/var/folders/2z/36b018md22j8c18318cmt1f00000gn/T//RtmpKujUSj/h2o_meierhabarexiti_started_from_r.out
/var/folders/2z/36b018md22j8c18318cmt1f00000gn/T//RtmpKujUSj/h2o_meierhabarexiti_started_from_r.errjava version "1.6.0_65"
Java(TM) SE Runtime Environment (build 1.6.0_65-b14-468-11M4833)
Java HotSpot(TM) 64-Bit Server VM (build 20.65-b04-468, mixed mode)
Starting H2O JVM and connecting: ... Connection successful!
R is connected to the H2O cluster:
H2O cluster uptime: 5 seconds 140 milliseconds
H2O cluster version: 3.10.4.6
H2O cluster version age: 1 month and 4 days
H2O cluster name: H2O_started_from_R_meierhabarexiti_wti833
H2O cluster total nodes: 1
H2O cluster total memory: 0.12 GB
H2O cluster total cores: 4
H2O cluster allowed cores: 2
H2O cluster healthy: TRUE
H2O Connection ip: localhost
H2O Connection port: 54321
H2O Connection proxy: NA
H2O Internal Security: FALSE
R Version: R version 3.3.1 (2016-06-21)
Note: As started, H2O is limited to the CRAN default of 2 CPUs.
Shut down and restart H2O as shown below to use all your CPUs.
> h2o.shutdown()
> h2o.init(nthreads = -1)letterdata.hex <- h2o.importFile("http://www.sci.csueastbay.edu/~esuess/classes/Statistics_6620/Presentations/ml11/letterdata.csv")
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|=================================================================================| 100%summary(letterdata.hex)Approximated quantiles computed! If you are interested in exact quantiles, please pass the `exact_quantiles=TRUE` parameter. letter xbox ybox width height
U:813 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
D:805 1st Qu.: 3.000 1st Qu.: 5.000 1st Qu.: 4.000 1st Qu.: 4.000
P:803 Median : 4.000 Median : 7.000 Median : 5.000 Median : 6.000
T:796 Mean : 4.024 Mean : 7.035 Mean : 5.122 Mean : 5.372
M:792 3rd Qu.: 5.000 3rd Qu.: 9.000 3rd Qu.: 6.000 3rd Qu.: 7.000
A:789 Max. :15.000 Max. :15.000 Max. :15.000 Max. :15.000
onpix xbar ybar x2bar y2bar
Min. : 0.000 Min. : 0.000 Min. : 0.0 Min. : 0.000 Min. : 0.000
1st Qu.: 2.000 1st Qu.: 6.000 1st Qu.: 6.0 1st Qu.: 3.000 1st Qu.: 4.000
Median : 3.000 Median : 7.000 Median : 7.0 Median : 4.000 Median : 5.000
Mean : 3.506 Mean : 6.898 Mean : 7.5 Mean : 4.629 Mean : 5.179
3rd Qu.: 5.000 3rd Qu.: 8.000 3rd Qu.: 9.0 3rd Qu.: 6.000 3rd Qu.: 7.000
Max. :15.000 Max. :15.000 Max. :15.0 Max. :15.000 Max. :15.000
xybar x2ybar xy2bar xedge xedgey
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 7.000 1st Qu.: 5.000 1st Qu.: 7.000 1st Qu.: 1.000 1st Qu.: 8.000
Median : 8.000 Median : 6.000 Median : 8.000 Median : 3.000 Median : 8.000
Mean : 8.282 Mean : 6.454 Mean : 7.929 Mean : 3.046 Mean : 8.339
3rd Qu.:10.000 3rd Qu.: 8.000 3rd Qu.: 9.000 3rd Qu.: 4.000 3rd Qu.: 9.000
Max. :15.000 Max. :15.000 Max. :15.000 Max. :15.000 Max. :15.000
yedge yedgex
Min. : 0.000 Min. : 0.000
1st Qu.: 2.000 1st Qu.: 7.000
Median : 3.000 Median : 8.000
Mean : 3.692 Mean : 7.801
3rd Qu.: 5.000 3rd Qu.: 9.000
Max. :15.000 Max. :15.000 splits <- h2o.splitFrame(letterdata.hex, 0.80, seed=1234)
dl <- h2o.deeplearning(x=2:17,y="letter",training_frame=splits[[1]],activation = "RectifierWithDropout",
hidden = c(16,16,16), distribution = "multinomial",input_dropout_ratio=0.2,
epochs = 10,nfold=5,variable_importances = TRUE)
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|============= | 16%
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|=========================== | 33%
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|========================================= | 51%
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|======================================================= | 68%
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|=================================================================== | 83%
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|========================================================================= | 90%
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|=================================================================================| 100%dl.predict <- h2o.predict (dl, splits[[2]])
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|=================================================================================| 100%dl@parameters$model_id
[1] "DeepLearning_model_R_1496288651976_1"
$training_frame
[1] "RTMP_sid_a9a3_2"
$nfolds
[1] 5
$overwrite_with_best_model
[1] FALSE
$activation
[1] "RectifierWithDropout"
$hidden
[1] 16 16 16
$epochs
[1] 10.40785
$seed
[1] -5.085292e+18
$input_dropout_ratio
[1] 0.2
$distribution
[1] "multinomial"
$stopping_rounds
[1] 0
$variable_importances
[1] TRUE
$x
[1] "xbox" "ybox" "width" "height" "onpix" "xbar" "ybar" "x2bar" "y2bar"
[10] "xybar" "x2ybar" "xy2bar" "xedge" "xedgey" "yedge" "yedgex"
$y
[1] "letter"h2o.performance(dl)H2OMultinomialMetrics: deeplearning
** Reported on training data. **
** Metrics reported on temporary training frame with 10076 samples **
Training Set Metrics:
=====================
MSE: (Extract with `h2o.mse`) 0.8354604
RMSE: (Extract with `h2o.rmse`) 0.9140352
Logloss: (Extract with `h2o.logloss`) 2.617066
Mean Per-Class Error: 0.831097
Confusion Matrix: Extract with `h2o.confusionMatrix(<model>,train = TRUE)`)
=========================================================================
Confusion Matrix: vertical: actual; across: predicted
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Error
A 117 6 0 12 0 0 3 0 0 0 0 0 2 0 0 2 0 224 8 24 0 0 0 0 0 0 0.7060
B 0 325 0 9 0 0 0 0 6 0 0 0 0 0 0 13 0 2 38 27 0 0 0 2 0 0 0.2299
C 0 25 0 9 0 1 4 0 7 0 0 0 1 0 0 1 0 0 234 38 1 0 0 61 0 0 1.0000
D 0 94 0 9 0 0 3 0 30 0 0 0 0 0 0 225 0 2 17 25 0 0 0 1 0 0 0.9778
E 0 88 0 0 0 0 3 0 1 0 0 0 0 0 0 1 0 0 272 13 0 0 0 6 0 2 1.0000
Rate
A = 281 / 398
B = 97 / 422
C = 382 / 382
D = 397 / 406
E = 386 / 386
---
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
V 0 1 0 9 0 0 0 0 0 0 0 0 1 0 0 12 0 0 1 360 0 0 0 0 0 0
W 0 0 0 2 0 0 0 0 0 0 0 0 5 6 0 10 0 0 1 311 0 56 3 0 0 0
X 0 74 0 3 0 0 0 0 53 0 0 0 0 0 0 0 0 0 176 71 0 0 0 4 0 0
Y 0 2 0 5 0 0 0 0 1 0 0 0 0 0 0 0 0 0 14 344 0 0 0 4 0 0
Z 0 35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 235 20 0 0 0 0 0 85
Totals 134 2368 0 271 0 2 109 0 431 0 0 0 372 69 0 778 0 316 1782 3060 8 91 10 175 0 100
Error Rate
V 1.0000 = 384 / 384
W 0.9924 = 391 / 394
X 0.9895 = 377 / 381
Y 1.0000 = 370 / 370
Z 0.7745 = 292 / 377
Totals 0.8264 = 8,327 / 10,076
Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>,train = TRUE)`
=======================================================================
Top-10 Hit Ratios:
k hit_ratio
1 1 0.173581
2 2 0.311731
3 3 0.411473
4 4 0.512703
5 5 0.575427
6 6 0.643112
7 7 0.714669
8 8 0.758833
9 9 0.789004
10 10 0.828702h2o.varimp(dl)Variable Importances:
variable relative_importance scaled_importance percentage
1 yedge 1.000000 1.000000 0.107052
2 xedge 0.902801 0.902801 0.096646
3 xy2bar 0.851448 0.851448 0.091149
4 x2ybar 0.813513 0.813513 0.087088
5 xedgey 0.769036 0.769036 0.082326
6 ybar 0.743807 0.743807 0.079626
7 y2bar 0.687911 0.687911 0.073642
8 x2bar 0.605888 0.605888 0.064861
9 yedgex 0.521135 0.521135 0.055788
10 onpix 0.499478 0.499478 0.053470
11 xbar 0.420246 0.420246 0.044988
12 width 0.376321 0.376321 0.040286
13 xybar 0.324365 0.324365 0.034724
14 ybox 0.303355 0.303355 0.032475
15 xbox 0.295416 0.295416 0.031625
16 height 0.226573 0.226573 0.024255h2o.shutdown()