请考虑以下基本可重复的示例：
library(h2o)
h2o.init()
data("iris")
iris.hex = as.h2o(iris, "iris.hex")
mod = h2o.glm(y = "Sepal.Length", x = setdiff(colnames(iris), "Sepal.Length"),
training_frame = iris.hex, nfolds = 2, seed = 100,
lambda_search = T, early_stopping = F,
family = "gamma", nlambdas = 100)
当我运行上述内容时，我希望h2o将迭代100多个不同的lambda值。但是，运行length(mod@allparameters$lambda)将显示实际只测试了79个lambda值。这79个值是序列中的前79个值：
maxLambda = max(mod@allparameters$lambda)
lambdaMinRatio = mod@allparameters$lambda_min_ratio
exp(seq(log(maxLambda), log(maxLambda*lambdaMinRatio), length.out = 100))
你能不能让我知道如何让函数迭代所有100个lambda值？ （我尝试设置early_stopping = F以确定是否可以解决问题，但事实并非如此。）
如果有帮助，这是我的群集信息：
R is connected to the H2O cluster:
H2O cluster uptime: 11 hours 21 minutes
H2O cluster version: 3.10.5.3
H2O cluster version age: 1 month and 26 days
H2O cluster name: H2O_started_from_R_xaq943
H2O cluster total nodes: 1
H2O cluster total memory: 6.75 GB
H2O cluster total cores: 8
H2O cluster allowed cores: 4
H2O cluster healthy: TRUE
H2O Connection ip: localhost
H2O Connection port: 54321
H2O Connection proxy: NA
H2O Internal Security: FALSE
R Version: R version 3.3.3 (2017-03-06)
谢谢！
编辑：根据要求，这里是h2o.getGLMFullRegularizationPath（mod）的输出：
$`__meta`
$`__meta`$schema_version
[1] 3
$`__meta`$schema_name
[1] "GLMRegularizationPathV3"
$`__meta`$schema_type
[1] "RegularizationPath"
$model
NULL
$lambdas
[1] 1.434114617 1.306711827 1.190627150 1.084855115 0.988479577 0.900665776 0.820653111 0.747748550 0.681320630 0.620793983 0.565644356
[12] 0.515394071 0.469607882 0.427889212 0.389876714 0.355241141 0.323682497 0.294927436 0.268726896 0.244853939 0.223101790 0.203282042
[23] 0.185223025 0.168768322 0.153775410 0.140114426 0.127667047 0.116325458 0.105991425 0.096575439 0.087995943 0.080178626 0.073055778
[34] 0.066565704 0.060652190 0.055264017 0.050354514 0.045881158 0.041805202 0.038091343 0.034707413 0.031624102 0.028814704 0.026254885
[45] 0.023922474 0.021797267 0.019860858 0.018096474 0.016488833 0.015024011 0.013689319 0.012473198 0.011365113 0.010355468 0.009435517
[56] 0.008597291 0.007833532 0.007137622 0.006503536 0.005925779 0.005399349 0.004919686 0.004482635 0.004084410 0.003721562 0.003390949
[67] 0.003089706 0.002815225 0.002565128 0.002337249 0.002129615 0.001940426 0.001768044 0.001610975 0.001467861 0.001337460 0.001218644
[78] 0.001110383 0.001011740
$explained_deviance_train
[1] -3.294962e-08 1.278780e-01 2.352402e-01 3.253159e-01 4.008369e-01 4.641126e-01 5.170944e-01 5.614293e-01 5.985067e-01
[10] 6.294974e-01 6.553869e-01 6.770044e-01 6.950464e-01 7.100979e-01 7.226495e-01 7.331127e-01 7.418320e-01 7.490957e-01
[19] 7.551451e-01 7.687710e-01 7.815713e-01 7.921910e-01 8.010014e-01 8.083105e-01 8.143741e-01 8.194045e-01 8.235584e-01
[28] 8.270239e-01 8.298991e-01 8.322847e-01 8.342640e-01 8.359064e-01 8.372692e-01 8.384000e-01 8.393384e-01 8.401172e-01
[37] 8.407634e-01 8.411713e-01 8.420553e-01 8.434391e-01 8.445680e-01 8.454431e-01 8.462240e-01 8.468835e-01 8.476350e-01
[46] 8.481135e-01 8.497288e-01 8.513965e-01 8.528687e-01 8.541499e-01 8.551259e-01 8.560063e-01 8.566711e-01 8.572853e-01
[55] 8.578407e-01 8.583362e-01 8.586877e-01 8.590151e-01 8.593148e-01 8.595864e-01 8.596849e-01 8.599377e-01 8.600233e-01
[64] 8.602430e-01 8.603153e-01 8.605097e-01 8.605776e-01 8.608212e-01 8.608821e-01 8.610499e-01 8.611065e-01 8.611627e-01
[73] 8.612156e-01 8.616241e-01 8.616940e-01 8.617575e-01 8.617782e-01 8.617988e-01 8.618557e-01
$explained_deviance_valid
NULL
$coefficients
Species.setosa Species.versicolor Species.virginica Sepal.Width Petal.Length Petal.Width Intercept
[1,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 0.000000000 0.000000e+00 0.1711352
[2,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.001046643 0.000000e+00 0.1750882
[3,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.002009314 0.000000e+00 0.1787588
[4,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.002894275 0.000000e+00 0.1821621
[5,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.003707356 0.000000e+00 0.1853133
[6,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.004453990 0.000000e+00 0.1882274
[7,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.005139245 0.000000e+00 0.1909189
[8,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.005767843 0.000000e+00 0.1934021
[9,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.006344186 0.000000e+00 0.1956907
[10,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.006872371 0.000000e+00 0.1977980
[11,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.007356208 0.000000e+00 0.1997366
[12,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.007799235 0.000000e+00 0.2015187
[13,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.008204738 0.000000e+00 0.2031555
[14,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.008575759 0.000000e+00 0.2046579
[15,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.008915116 0.000000e+00 0.2060361
[16,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.009225414 0.000000e+00 0.2072996
[17,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.009509059 0.000000e+00 0.2084574
[18,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.009768269 0.000000e+00 0.2095177
[19,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.010005092 0.000000e+00 0.2104884
[20,] 0.000000e+00 0.0000000000 0.000000e+00 -0.001127417 -0.010319589 0.000000e+00 0.2151915
[21,] 0.000000e+00 0.0000000000 0.000000e+00 -0.002359216 -0.010623694 0.000000e+00 0.2201719
[22,] 0.000000e+00 0.0000000000 0.000000e+00 -0.003480564 -0.010900376 0.000000e+00 0.2247086
[23,] 0.000000e+00 0.0000000000 0.000000e+00 -0.004501465 -0.011152087 0.000000e+00 0.2288412
[24,] 0.000000e+00 0.0000000000 0.000000e+00 -0.005430894 -0.011381098 0.000000e+00 0.2326054
[25,] 0.000000e+00 0.0000000000 0.000000e+00 -0.006277042 -0.011589469 0.000000e+00 0.2360339
[26,] 0.000000e+00 0.0000000000 0.000000e+00 -0.007047377 -0.011779076 0.000000e+00 0.2391565
[27,] 0.000000e+00 0.0000000000 0.000000e+00 -0.007743794 -0.011951139 0.000000e+00 0.2419836
[28,] 0.000000e+00 0.0000000000 0.000000e+00 -0.008382717 -0.012108203 0.000000e+00 0.2445752
[29,] 0.000000e+00 0.0000000000 0.000000e+00 -0.008964450 -0.012251159 0.000000e+00 0.2469356
[30,] 0.000000e+00 0.0000000000 0.000000e+00 -0.009494120 -0.012381280 0.000000e+00 0.2490854
[31,] 0.000000e+00 0.0000000000 0.000000e+00 -0.009976404 -0.012499729 0.000000e+00 0.2510434
[32,] 0.000000e+00 0.0000000000 0.000000e+00 -0.010415558 -0.012607559 0.000000e+00 0.2528268
[33,] 0.000000e+00 0.0000000000 0.000000e+00 -0.010815455 -0.012705728 0.000000e+00 0.2544511
[34,] 0.000000e+00 0.0000000000 0.000000e+00 -0.011179617 -0.012795108 0.000000e+00 0.2559306
[35,] 0.000000e+00 0.0000000000 0.000000e+00 -0.011511250 -0.012876490 0.000000e+00 0.2572783
[36,] 0.000000e+00 0.0000000000 0.000000e+00 -0.011813271 -0.012950594 0.000000e+00 0.2585058
[37,] 0.000000e+00 0.0000000000 0.000000e+00 -0.012088333 -0.013018075 0.000000e+00 0.2596239
[38,] 0.000000e+00 0.0000000000 0.000000e+00 -0.012254270 -0.013069805 0.000000e+00 0.2603445
[39,] 0.000000e+00 -0.0001175922 0.000000e+00 -0.012623025 -0.013136288 0.000000e+00 0.2617830
[40,] 0.000000e+00 -0.0005066170 0.000000e+00 -0.013031762 -0.013198821 0.000000e+00 0.2634171
[41,] 0.000000e+00 -0.0008532154 0.000000e+00 -0.013400532 -0.013255288 0.000000e+00 0.2648907
[42,] 0.000000e+00 -0.0011428955 0.000000e+00 -0.013718316 -0.013304258 0.000000e+00 0.2661590
[43,] 0.000000e+00 -0.0014293556 0.000000e+00 -0.014023516 -0.013351005 0.000000e+00 0.2673789
[44,] 0.000000e+00 -0.0016797073 1.243120e-05 -0.014304179 -0.013396541 0.000000e+00 0.2685020
[45,] 0.000000e+00 -0.0018706468 9.790433e-05 -0.014536882 -0.013478186 8.361933e-05 0.2694643
[46,] 0.000000e+00 -0.0019698629 1.717337e-04 -0.014665554 -0.013530772 1.814935e-04 0.2699431
[47,] 0.000000e+00 -0.0021078477 2.246836e-04 -0.014925921 -0.013849890 8.489923e-04 0.2711751
[48,] 0.000000e+00 -0.0021556371 3.034315e-04 -0.015150706 -0.014237748 1.656453e-03 0.2723528
[49,] 0.000000e+00 -0.0021453273 4.458210e-04 -0.015348300 -0.014616464 2.413512e-03 0.2734328
[50,] 0.000000e+00 -0.0020839569 6.461732e-04 -0.015520020 -0.014980050 3.109852e-03 0.2744131
[51,] 0.000000e+00 -0.0020107174 8.597081e-04 -0.015660412 -0.015278421 3.659515e-03 0.2752178
[52,] 0.000000e+00 -0.0019078474 1.101906e-03 -0.015786052 -0.015572930 4.186424e-03 0.2759708
[53,] 0.000000e+00 -0.0018175109 1.323132e-03 -0.015890456 -0.015809763 4.599076e-03 0.2765883
[54,] 0.000000e+00 -0.0017094991 1.558056e-03 -0.015986195 -0.016047486 5.006251e-03 0.2771791
[55,] 0.000000e+00 -0.0015842081 1.807162e-03 -0.016071634 -0.016281094 5.397220e-03 0.2777320
[56,] 0.000000e+00 -0.0014430021 2.070103e-03 -0.016146458 -0.016507391 5.765349e-03 0.2782422
[57,] 0.000000e+00 -0.0013372850 2.282679e-03 -0.016207766 -0.016676301 6.033973e-03 0.2786413
[58,] 0.000000e+00 -0.0012235170 2.499826e-03 -0.016264638 -0.016845566 6.300372e-03 0.2790268
[59,] 0.000000e+00 -0.0011012638 2.721871e-03 -0.016315538 -0.017012360 6.558645e-03 0.2793901
[60,] 0.000000e+00 -0.0009710435 2.949010e-03 -0.016360197 -0.017174819 6.804753e-03 0.2797282
[61,] 0.000000e+00 -0.0009387214 3.037293e-03 -0.016389637 -0.017231436 6.890089e-03 0.2798895
[62,] 0.000000e+00 -0.0008039241 3.270133e-03 -0.016434978 -0.017400280 7.145560e-03 0.2802397
[63,] 0.000000e+00 -0.0007660395 3.357739e-03 -0.016459753 -0.017456898 7.230221e-03 0.2803861
[64,] 0.000000e+00 -0.0006199118 3.595215e-03 -0.016496474 -0.017622417 7.475811e-03 0.2807040
[65,] 0.000000e+00 -0.0005768718 3.683036e-03 -0.016516321 -0.017676868 7.554952e-03 0.2808322
[66,] -3.476645e-05 -0.0004234267 3.926329e-03 -0.016530611 -0.017839966 7.778584e-03 0.2811053
[67,] -6.891206e-05 -0.0003785185 4.015034e-03 -0.016531687 -0.017896456 7.848434e-03 0.2812048
[68,] -2.509409e-04 -0.0001852860 4.347389e-03 -0.016506532 -0.018118743 8.088206e-03 0.2815679
[69,] -3.133552e-04 -0.0001451602 4.438172e-03 -0.016500367 -0.018173876 8.139393e-03 0.2816729
[70,] -5.214018e-04 -0.0000198928 4.695164e-03 -0.016468891 -0.018337882 8.275330e-03 0.2819765
[71,] -6.024159e-04 0.0000000000 4.785344e-03 -0.016466374 -0.018391875 8.314552e-03 0.2821158
[72,] -6.921978e-04 0.0000000000 4.869432e-03 -0.016471686 -0.018446669 8.353211e-03 0.2822946
[73,] -7.920269e-04 0.0000000000 4.942796e-03 -0.016472703 -0.018501428 8.391136e-03 0.2824681
[74,] -2.055117e-03 0.0000000000 5.491157e-03 -0.016310937 -0.018964797 8.523048e-03 0.2838078
[75,] -2.353043e-03 0.0000000000 5.606834e-03 -0.016260884 -0.019047344 8.505333e-03 0.2840483
[76,] -2.644396e-03 0.0000000000 5.720820e-03 -0.016211592 -0.019126493 8.483952e-03 0.2842812
[77,] -2.743107e-03 0.0000000000 5.760265e-03 -0.016195310 -0.019153151 8.477521e-03 0.2843592
[78,] -2.843096e-03 0.0000000000 5.800458e-03 -0.016179171 -0.019181275 8.473083e-03 0.2844411
[79,] -3.135365e-03 0.0000000000 5.915736e-03 -0.016130870 -0.019263792 8.457283e-03 0.2846831
$coefficients_std
Species.setosa Species.versicolor Species.virginica Sepal.Width Petal.Length Petal.Width Intercept
[1,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 0.000000000 0.0000000000 0.1711352
[2,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.001847636 0.0000000000 0.1711550
[3,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.003547039 0.0000000000 0.1712078
[4,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.005109259 0.0000000000 0.1712854
[5,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.006544589 0.0000000000 0.1713811
[6,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.007862621 0.0000000000 0.1714893
[7,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.009072299 0.0000000000 0.1716056
[8,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.010181963 0.0000000000 0.1717265
[9,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.011199381 0.0000000000 0.1718492
[10,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.012131784 0.0000000000 0.1719716
[11,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.012985900 0.0000000000 0.1720920
[12,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.013767976 0.0000000000 0.1722091
[13,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.014483810 0.0000000000 0.1723221
[14,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.015138773 0.0000000000 0.1724302
[15,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.015737839 0.0000000000 0.1725331
[16,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.016285607 0.0000000000 0.1726305
[17,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.016786324 0.0000000000 0.1727224
[18,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.017243908 0.0000000000 0.1728086
[19,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.017661971 0.0000000000 0.1728892
[20,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0004914029 -0.018217153 0.0000000000 0.1729636
[21,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0010283025 -0.018753989 0.0000000000 0.1730351
[22,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0015170607 -0.019242414 0.0000000000 0.1731038
[23,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0019620369 -0.019686760 0.0000000000 0.1731692
[24,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0023671437 -0.020091032 0.0000000000 0.1732312
[25,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0027359511 -0.020458869 0.0000000000 0.1732897
[26,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0030717141 -0.020793582 0.0000000000 0.1733446
[27,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0033752589 -0.021097325 0.0000000000 0.1733959
[28,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0036537438 -0.021374590 0.0000000000 0.1734438
[29,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0039073015 -0.021626949 0.0000000000 0.1734884
[30,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0041381667 -0.021856652 0.0000000000 0.1735299
[31,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0043483781 -0.022065749 0.0000000000 0.1735682
[32,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0045397906 -0.022256101 0.0000000000 0.1736038
[33,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0047140920 -0.022429399 0.0000000000 0.1736366
[34,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0048728180 -0.022587182 0.0000000000 0.1736668
[35,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0050173658 -0.022730846 0.0000000000 0.1736947
[36,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0051490065 -0.022861661 0.0000000000 0.1737203
[37,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0052688970 -0.022980784 0.0000000000 0.1737439
[38,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0053412234 -0.023072104 0.0000000000 0.1737627
[39,] 0.000000e+00 -0.0001175922 0.000000e+00 -0.0055019511 -0.023189466 0.0000000000 0.1738240
[40,] 0.000000e+00 -0.0005066170 0.000000e+00 -0.0056801058 -0.023299856 0.0000000000 0.1739735
[41,] 0.000000e+00 -0.0008532154 0.000000e+00 -0.0058408402 -0.023399537 0.0000000000 0.1741074
[42,] 0.000000e+00 -0.0011428955 0.000000e+00 -0.0059793516 -0.023485982 0.0000000000 0.1742201
[43,] 0.000000e+00 -0.0014293556 0.000000e+00 -0.0061123779 -0.023568506 0.0000000000 0.1743313
[44,] 0.000000e+00 -0.0016797073 1.243120e-05 -0.0062347093 -0.023648890 0.0000000000 0.1744252
[45,] 0.000000e+00 -0.0018706468 9.790433e-05 -0.0063361369 -0.023793017 0.0000637378 0.1744695
[46,] 0.000000e+00 -0.0019698629 1.717337e-04 -0.0063922205 -0.023885848 0.0001383412 0.1744746
[47,] 0.000000e+00 -0.0021078477 2.246836e-04 -0.0065057056 -0.024449186 0.0006471339 0.1745119
[48,] 0.000000e+00 -0.0021556371 3.034315e-04 -0.0066036820 -0.025133872 0.0012626110 0.1745132
[49,] 0.000000e+00 -0.0021453273 4.458210e-04 -0.0066898067 -0.025802418 0.0018396700 0.1744739
[50,] 0.000000e+00 -0.0020839569 6.461732e-04 -0.0067646533 -0.026444255 0.0023704464 0.1743980
[51,] 0.000000e+00 -0.0020107174 8.597081e-04 -0.0068258458 -0.026970969 0.0027894199 0.1743114
[52,] 0.000000e+00 -0.0019078474 1.101906e-03 -0.0068806078 -0.027490866 0.0031910501 0.1742055
[53,] 0.000000e+00 -0.0018175109 1.323132e-03 -0.0069261140 -0.027908946 0.0035055892 0.1741087
[54,] 0.000000e+00 -0.0017094991 1.558056e-03 -0.0069678433 -0.028328598 0.0038159527 0.1740017
[55,] 0.000000e+00 -0.0015842081 1.807162e-03 -0.0070050834 -0.028740986 0.0041139641 0.1738844
[56,] 0.000000e+00 -0.0014430021 2.070103e-03 -0.0070376966 -0.029140468 0.0043945661 0.1737569
[57,] 0.000000e+00 -0.0013372850 2.282679e-03 -0.0070644186 -0.029438645 0.0045993214 0.1736560
[58,] 0.000000e+00 -0.0012235170 2.499826e-03 -0.0070892073 -0.029737447 0.0048023811 0.1735510
[59,] 0.000000e+00 -0.0011012638 2.721871e-03 -0.0071113928 -0.030031888 0.0049992462 0.1734416
[60,] 0.000000e+00 -0.0009710435 2.949010e-03 -0.0071308582 -0.030318677 0.0051868390 0.1733278
[61,] 0.000000e+00 -0.0009387214 3.037293e-03 -0.0071436900 -0.030418624 0.0052518855 0.1732887
[62,] 0.000000e+00 -0.0008039241 3.270133e-03 -0.0071634528 -0.030716683 0.0054466147 0.1731721
[63,] 0.000000e+00 -0.0007660395 3.357739e-03 -0.0071742512 -0.030816631 0.0055111469 0.1731316
[64,] 0.000000e+00 -0.0006199118 3.595215e-03 -0.0071902568 -0.031108822 0.0056983444 0.1730097
[65,] 0.000000e+00 -0.0005768718 3.683036e-03 -0.0071989073 -0.031204944 0.0057586692 0.1729675
[66,] -3.476645e-05 -0.0004234267 3.926329e-03 -0.0072051358 -0.031492861 0.0059291294 0.1728522
[67,] -6.891206e-05 -0.0003785185 4.015034e-03 -0.0072056052 -0.031592582 0.0059823720 0.1728199
[68,] -2.509409e-04 -0.0001852860 4.347389e-03 -0.0071946407 -0.031984985 0.0061651351 0.1727122
[69,] -3.133552e-04 -0.0001451602 4.438172e-03 -0.0071919537 -0.032082312 0.0062041522 0.1726902
[70,] -5.214018e-04 -0.0000198928 4.695164e-03 -0.0071782342 -0.032371830 0.0063077682 0.1726367
[71,] -6.024159e-04 0.0000000000 4.785344e-03 -0.0071771375 -0.032467144 0.0063376645 0.1726279
[72,] -6.921978e-04 0.0000000000 4.869432e-03 -0.0071794528 -0.032563872 0.0063671323 0.1726309
[73,] -7.920269e-04 0.0000000000 4.942796e-03 -0.0071798959 -0.032660538 0.0063960401 0.1726409
[74,] -2.055117e-03 0.0000000000 5.491157e-03 -0.0071093875 -0.033478523 0.0064965885 0.1728921
[75,] -2.353043e-03 0.0000000000 5.606834e-03 -0.0070875711 -0.033624243 0.0064830849 0.1729542
[76,] -2.644396e-03 0.0000000000 5.720820e-03 -0.0070660864 -0.033763964 0.0064667874 0.1730147
[77,] -2.743107e-03 0.0000000000 5.760265e-03 -0.0070589896 -0.033811024 0.0064618858 0.1730346
[78,] -2.843096e-03 0.0000000000 5.800458e-03 -0.0070519551 -0.033860671 0.0064585028 0.1730548
[79,] -3.135365e-03 0.0000000000 5.915736e-03 -0.0070309023 -0.034006338 0.0064464596 0.1731155
$coefficient_names
[1] "Species.setosa" "Species.versicolor" "Species.virginica" "Sepal.Width" "Petal.Length" "Petal.Width"
[7] "Intercept"
> tail(mx@allparameters$lambda)
[1] 0.1536665 0.1400152 0.1275767 0.1162431 0.1059164
> mx@model$lambda_best
[1] 0.1059164
>
> lapply(mx@model$cross_validation_models, function(m_cv){
+ m <- h2o.getModel(m_cv$name)
+ list( tail(m@allparameters$lambda), m@model$lambda_best )
+ })
[[1]]
[[1]][[1]]
[1] 2.446806e-05 2.229438e-05 2.031381e-05 1.850919e-05 1.686488e-05 1.536665e-05
[[1]][[2]]
[1] 0.01135707
[[2]]
[[2]][[1]]
[1] 2.446806e-05 2.229438e-05 2.031381e-05 1.850919e-05 1.686488e-05 1.536665e-05
[[2]][[2]]
[1] 0.01808366
[[3]]
[[3]][[1]]
[1] 2.446806e-05 2.229438e-05 2.031381e-05 1.850919e-05 1.686488e-05 1.536665e-05
[[3]][[2]]
[1] 0.01647716
答案 0 :(得分：4)
简短回答：您发现了一个错误，我们已经开了一张机票here。 nfolds＆gt;时，不会遵守提前停止标志。 0.与此同时，如果你没有设置nfolds，你应该得到100个lambdas。
答案 1 :(得分：1)
正在发生的事情是它从交叉验证模型中学习，以优化用于最终运行的参数。 （顺便说一句，你使用type Transform<T> = (o: T) => T;
type Item = { /* properties */ };
transform(input, transformers: Transform<Item>[]) {
const items: Item[] = getItems(input);
return items.map(item => {
let transformed = item;
tramsformers.forEach(t => transformed = t(transformed));
return transformed;
})
}
这对于一个小数据集是相当不寻常的：只学习75条记录，然后测试另外75条记录。所以你从CV中学到的东西会有很多噪音。）
从您的代码开始：
nfolds=2我使用3.14.0.1，所以这就是我得到的：
tail(mod@allparameters$lambda)
mod@model$lambda_best
和
[1] 0.002129615 0.001940426 0.001768044 0.001610975 0.001467861 0.001337460
然后，如果我们去看看2个CV模型的相同内容：
[1] 0.001610975
我明白了：
lapply(mod@model$cross_validation_models, function(m_cv){
m <- h2o.getModel(m_cv$name)
list( tail(m@allparameters$lambda), m@model$lambda_best )
})
即。似乎在CV模型中找到的最低lambda值为0.00133，所以它已经将其用作最终模型的早期停止。
顺便说一句，如果你在那些cv模型中四处寻找，你会看到他们都为lambda尝试了100个值。只有最终模型才能进行额外的优化。（我将其视为时间优化，但是阅读广义线性模型手册的第26/27页（从https://www.h2o.ai/resources/免费下载），我认为这主要是关于使用简历数据，以避免过度拟合。）
你可以显式指定一组要尝试的lambda值。但是，交叉验证学习仍将优先考虑最终模型。例如。在下面的最后一个模型只尝试了我建议的6个lambda值中的前4个，因为两个CV模型都最喜欢0.001。
[[1]]
[[1]][[1]]
[1] 0.0002283516 0.0002080655 0.0001895815 0.0001727396 0.0001573939 0.0001434115
[[1]][[2]]
[1] 0.002337249
[[2]]
[[2]][[1]]
[1] 0.0002283516 0.0002080655 0.0001895815 0.0001727396 0.0001573939 0.0001434115
[[2]][[2]]
[1] 0.00133746