GLM with Elastic Net Regularization Survival Learner
mlr_learners_surv.glmnet.RdGeneralized linear models with elastic net regularization.
Calls glmnet::glmnet() from package glmnet.
Prediction types
This learner returns three prediction types:
lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated usingglmnet::predict.coxnet().crank: same aslp.distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated usingglmnet::survfit.coxnet(). Parametersstypeandctyperelate to howlppredictions are transformed into survival predictions and are described insurvival::survfit.coxph(). By default the Breslow estimator is used for computing the baseline hazard.
Caution: This learner is different to learners calling glmnet::cv.glmnet()
in that it does not use the internal optimization of parameter lambda.
Instead, lambda needs to be tuned by the user (e.g., via mlr3tuning).
When lambda is tuned, the glmnet will be trained for each tuning iteration.
While fitting the whole path of lambdas would be more efficient, as is done
by default in glmnet::glmnet(), tuning/selecting the parameter at prediction time
(using parameter s) is currently not supported in mlr3
(at least not in efficient manner).
Tuning the s parameter is, therefore, currently discouraged.
When the data are i.i.d. and efficiency is key, we recommend using the respective
auto-tuning counterpart in mlr_learners_surv.cv_glmnet().
However, in some situations this is not applicable, usually when data are
imbalanced or not i.i.d. (longitudinal, time-series) and tuning requires
custom resampling strategies (blocked design, stratification).
Meta Information
Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, glmnet
Parameters
| Id | Type | Default | Levels | Range |
| alignment | character | lambda | lambda, fraction | - |
| alpha | numeric | 1 | \([0, 1]\) | |
| big | numeric | 9.9e+35 | \((-\infty, \infty)\) | |
| devmax | numeric | 0.999 | \([0, 1]\) | |
| dfmax | integer | - | \([0, \infty)\) | |
| eps | numeric | 1e-06 | \([0, 1]\) | |
| epsnr | numeric | 1e-08 | \([0, 1]\) | |
| exact | logical | FALSE | TRUE, FALSE | - |
| exclude | untyped | - | - | |
| exmx | numeric | 250 | \((-\infty, \infty)\) | |
| fdev | numeric | 1e-05 | \([0, 1]\) | |
| gamma | untyped | - | - | |
| grouped | logical | TRUE | TRUE, FALSE | - |
| intercept | logical | TRUE | TRUE, FALSE | - |
| keep | logical | FALSE | TRUE, FALSE | - |
| lambda | untyped | - | - | |
| lambda.min.ratio | numeric | - | \([0, 1]\) | |
| lower.limits | untyped | -Inf | - | |
| maxit | integer | 100000 | \([1, \infty)\) | |
| mnlam | integer | 5 | \([1, \infty)\) | |
| mxit | integer | 100 | \([1, \infty)\) | |
| mxitnr | integer | 25 | \([1, \infty)\) | |
| newoffset | untyped | - | - | |
| nlambda | integer | 100 | \([1, \infty)\) | |
| offset | untyped | NULL | - | |
| parallel | logical | FALSE | TRUE, FALSE | - |
| penalty.factor | untyped | - | - | |
| pmax | integer | - | \([0, \infty)\) | |
| pmin | numeric | 1e-09 | \([0, 1]\) | |
| prec | numeric | 1e-10 | \((-\infty, \infty)\) | |
| predict.gamma | numeric | gamma.1se | \((-\infty, \infty)\) | |
| relax | logical | FALSE | TRUE, FALSE | - |
| s | numeric | 0.01 | \([0, \infty)\) | |
| standardize | logical | TRUE | TRUE, FALSE | - |
| thresh | numeric | 1e-07 | \([0, \infty)\) | |
| trace.it | integer | 0 | \([0, 1]\) | |
| type.logistic | character | Newton | Newton, modified.Newton | - |
| type.multinomial | character | ungrouped | ungrouped, grouped | - |
| upper.limits | untyped | Inf | - | |
| stype | integer | 2 | \([1, 2]\) | |
| ctype | integer | - | \([1, 2]\) |
References
Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1–22. doi:10.18637/jss.v033.i01 .
See also
as.data.table(mlr_learners)for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGlmnet
Methods
Method selected_features()
Returns the set of selected features as reported by glmnet::predict.glmnet()
with type set to "nonzero".
Arguments
lambda(
numeric(1))
Customlambda, defaults to the active lambda depending on parameter set.
Returns
(character()) of feature names.
Examples
# Define the Learner
learner = mlr3::lrn("surv.glmnet")
print(learner)
#> <LearnerSurvGlmnet:surv.glmnet>: Regularized Generalized Linear Model
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, glmnet
#> * Predict Types: [crank], distr, lp
#> * Feature Types: logical, integer, numeric
#> * Properties: selected_features, weights
# Define a Task
task = mlr3::tsk("grace")
# Create train and test set
ids = mlr3::partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
print(learner$model)
#> $model
#>
#> Call: (if (cv) glmnet::cv.glmnet else glmnet::glmnet)(x = data, y = target, family = "cox")
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.177700
#> 2 2 0.58 0.161900
#> 3 2 1.56 0.147500
#> 4 2 2.40 0.134400
#> 5 3 5.80 0.122500
#> 6 3 8.29 0.111600
#> 7 4 10.18 0.101700
#> 8 4 11.70 0.092640
#> 9 4 12.93 0.084410
#> 10 4 13.93 0.076910
#> 11 4 14.77 0.070080
#> 12 4 15.45 0.063850
#> 13 4 16.03 0.058180
#> 14 4 16.51 0.053010
#> 15 4 16.92 0.048300
#> 16 5 17.27 0.044010
#> 17 5 17.58 0.040100
#> 18 5 17.84 0.036540
#> 19 6 18.07 0.033290
#> 20 6 18.27 0.030340
#> 21 6 18.44 0.027640
#> 22 6 18.59 0.025190
#> 23 6 18.71 0.022950
#> 24 6 18.82 0.020910
#> 25 6 18.90 0.019050
#> 26 6 18.98 0.017360
#> 27 6 19.04 0.015820
#> 28 6 19.09 0.014410
#> 29 6 19.14 0.013130
#> 30 6 19.17 0.011970
#> 31 6 19.21 0.010900
#> 32 6 19.23 0.009934
#> 33 6 19.25 0.009051
#> 34 6 19.27 0.008247
#> 35 6 19.29 0.007515
#> 36 6 19.30 0.006847
#> 37 6 19.31 0.006239
#> 38 6 19.32 0.005684
#> 39 6 19.33 0.005179
#> 40 6 19.33 0.004719
#> 41 6 19.34 0.004300
#> 42 6 19.34 0.003918
#> 43 6 19.35 0.003570
#> 44 6 19.35 0.003253
#>
#> $x
#> age los revasc revascdays stchange sysbp
#> [1,] 32 5 1 0 1 121
#> [2,] 33 2 0 2 0 150
#> [3,] 35 5 1 2 0 172
#> [4,] 35 10 1 9 0 106
#> [5,] 34 5 0 5 0 120
#> [6,] 35 2 0 180 0 121
#> [7,] 35 2 1 1 1 112
#> [8,] 37 9 0 180 1 151
#> [9,] 38 13 1 0 1 161
#> [10,] 38 2 0 115 0 150
#> [11,] 35 0 0 180 1 119
#> [12,] 38 12 1 8 1 120
#> [13,] 36 5 1 0 1 115
#> [14,] 33 6 1 1 1 115
#> [15,] 40 12 1 9 0 153
#> [16,] 42 3 1 1 1 130
#> [17,] 38 5 1 3 0 125
#> [18,] 42 2 0 2 0 140
#> [19,] 40 11 1 10 1 120
#> [20,] 43 3 1 0 1 100
#> [21,] 41 2 1 1 0 166
#> [22,] 43 4 1 0 1 130
#> [23,] 42 4 0 180 0 162
#> [24,] 40 3 1 1 0 170
#> [25,] 42 12 1 10 1 170
#> [26,] 43 2 1 1 1 116
#> [27,] 44 5 1 1 0 170
#> [28,] 45 3 0 180 1 154
#> [29,] 45 9 1 7 0 110
#> [30,] 44 2 1 1 1 150
#> [31,] 43 2 0 180 1 140
#> [32,] 46 15 0 180 0 120
#> [33,] 46 2 1 1 0 126
#> [34,] 48 15 0 180 1 160
#> [35,] 45 3 0 150 0 130
#> [36,] 46 7 1 2 0 166
#> [37,] 46 13 1 10 0 100
#> [38,] 43 3 1 0 1 124
#> [39,] 45 8 1 0 1 117
#> [40,] 49 5 0 73 1 136
#> [41,] 45 5 0 5 0 141
#> [42,] 46 6 1 0 1 100
#> [43,] 47 2 0 180 0 108
#> [44,] 44 9 1 8 1 135
#> [45,] 45 5 0 180 1 190
#> [46,] 46 4 0 180 1 121
#> [47,] 44 2 0 180 0 142
#> [48,] 45 9 1 0 1 145
#> [49,] 47 3 1 1 1 120
#> [50,] 48 3 0 180 0 154
#> [51,] 47 5 1 3 1 130
#> [52,] 47 9 1 6 0 170
#> [53,] 46 3 1 0 1 119
#> [54,] 49 4 0 180 0 117
#> [55,] 47 10 0 10 1 140
#> [56,] 50 1 1 0 1 129
#> [57,] 48 2 1 0 0 184
#> [58,] 47 7 0 180 0 145
#> [59,] 50 4 1 1 0 125
#> [60,] 49 7 1 7 1 110
#> [61,] 46 9 1 9 1 122
#> [62,] 49 2 0 2 0 105
#> [63,] 51 1 0 1 1 145
#> [64,] 49 15 1 11 1 160
#> [65,] 46 6 1 0 1 156
#> [66,] 52 2 0 180 1 170
#> [67,] 50 7 1 0 1 92
#> [68,] 51 3 1 2 0 113
#> [69,] 49 7 1 4 1 90
#> [70,] 47 8 0 180 0 160
#> [71,] 51 8 0 180 1 140
#> [72,] 52 2 0 180 0 155
#> [73,] 48 7 1 0 1 110
#> [74,] 53 8 0 36 1 160
#> [75,] 48 17 1 10 0 111
#> [76,] 47 2 1 1 0 110
#> [77,] 52 4 1 4 0 152
#> [78,] 49 9 1 3 0 102
#> [79,] 49 15 0 180 1 160
#> [80,] 54 17 1 12 1 102
#> [81,] 53 7 1 0 0 199
#> [82,] 54 6 1 3 0 129
#> [83,] 51 3 1 1 0 140
#> [84,] 50 2 0 5 1 106
#> [85,] 50 10 1 6 0 122
#> [86,] 50 14 1 13 0 170
#> [87,] 51 25 1 1 0 202
#> [88,] 49 5 1 2 1 150
#> [89,] 53 4 0 4 0 140
#> [90,] 52 14 1 7 1 200
#> [91,] 48 6 0 180 0 160
#> [92,] 53 4 1 0 1 156
#> [93,] 54 9 1 0 1 138
#> [94,] 49 16 0 16 0 125
#> [95,] 54 23 1 10 0 131
#> [96,] 52 7 1 2 0 154
#> [97,] 55 6 1 2 1 114
#> [98,] 54 9 1 1 0 130
#> [99,] 55 4 1 2 0 150
#> [100,] 52 4 0 180 1 180
#> [101,] 51 13 1 11 0 145
#> [102,] 50 5 1 4 1 150
#> [103,] 54 4 1 0 1 121
#> [104,] 50 3 0 174 1 153
#> [105,] 49 1 0 1 1 110
#> [106,] 50 7 1 1 0 156
#> [107,] 53 9 0 9 1 95
#> [108,] 53 8 1 0 1 130
#> [109,] 50 7 1 0 1 127
#> [110,] 56 4 1 1 1 130
#> [111,] 52 5 0 175 1 117
#> [112,] 54 1 0 180 0 162
#> [113,] 54 7 1 0 1 100
#> [114,] 56 2 0 180 0 132
#> [115,] 55 5 1 4 1 120
#> [116,] 52 8 0 180 0 119
#> [117,] 53 18 1 9 1 150
#> [118,] 52 16 0 16 0 152
#> [119,] 52 16 1 14 0 170
#> [120,] 53 4 0 180 1 150
#> [121,] 55 6 0 180 1 100
#> [122,] 55 6 1 5 1 138
#> [123,] 54 12 1 0 1 190
#> [124,] 55 2 0 134 1 140
#> [125,] 55 1 0 2 0 130
#> [126,] 57 3 0 3 0 120
#> [127,] 54 7 1 2 0 129
#> [128,] 54 2 1 1 0 135
#> [129,] 54 2 1 1 1 176
#> [130,] 57 1 0 180 1 156
#> [131,] 56 14 1 11 0 130
#> [132,] 57 10 0 180 1 170
#> [133,] 58 8 0 8 1 130
#> [134,] 55 3 1 1 1 156
#> [135,] 57 0 0 0 1 150
#> [136,] 53 21 1 13 1 130
#> [137,] 57 4 0 180 1 119
#> [138,] 58 6 1 0 1 90
#> [139,] 55 9 1 2 1 147
#> [140,] 56 5 0 5 1 150
#> [141,] 54 23 1 8 0 120
#> [142,] 57 4 1 2 1 185
#> [143,] 53 4 0 147 1 145
#> [144,] 53 7 1 0 1 120
#> [145,] 57 11 1 10 1 129
#> [146,] 55 5 0 5 1 131
#> [147,] 54 7 1 0 1 141
#> [148,] 56 4 0 4 0 164
#> [149,] 59 15 1 10 0 140
#> [150,] 58 9 1 0 1 180
#> [151,] 55 5 1 0 0 140
#> [152,] 56 7 1 5 1 120
#> [153,] 59 9 1 1 1 125
#> [154,] 57 1 0 180 0 148
#> [155,] 60 11 1 9 0 106
#> [156,] 59 3 0 180 0 120
#> [157,] 57 2 0 2 1 120
#> [158,] 60 5 1 1 0 138
#> [159,] 58 11 1 9 1 124
#> [160,] 55 5 1 0 1 160
#> [161,] 57 10 1 9 0 103
#> [162,] 59 4 1 0 1 152
#> [163,] 58 26 1 0 1 189
#> [164,] 60 0 1 0 1 80
#> [165,] 59 2 1 1 0 140
#> [166,] 58 8 0 161 1 140
#> [167,] 58 14 1 6 0 190
#> [168,] 61 4 1 3 0 151
#> [169,] 61 9 1 8 0 150
#> [170,] 58 1 0 1 1 100
#> [171,] 61 20 1 13 0 130
#> [172,] 57 2 1 0 1 116
#> [173,] 57 4 1 3 0 138
#> [174,] 61 3 0 17 0 143
#> [175,] 56 14 0 45 0 130
#> [176,] 57 3 1 2 0 120
#> [177,] 56 13 1 6 1 158
#> [178,] 56 18 1 11 1 165
#> [179,] 58 11 0 172 1 135
#> [180,] 56 8 1 8 0 120
#> [181,] 57 1 0 1 0 126
#> [182,] 57 15 1 13 1 110
#> [183,] 59 5 1 2 0 182
#> [184,] 58 5 1 1 1 135
#> [185,] 59 10 0 180 0 160
#> [186,] 61 8 0 77 0 120
#> [187,] 62 10 1 0 1 153
#> [188,] 58 8 1 3 1 150
#> [189,] 60 7 0 7 0 147
#> [190,] 59 13 1 2 0 198
#> [191,] 57 12 1 9 1 120
#> [192,] 62 4 1 0 0 160
#> [193,] 58 3 1 0 1 146
#> [194,] 62 4 1 3 0 173
#> [195,] 63 6 0 28 1 120
#> [196,] 61 13 0 13 0 120
#> [197,] 61 5 0 5 1 110
#> [198,] 57 18 1 9 1 93
#> [199,] 58 11 1 9 0 179
#> [200,] 57 2 1 1 0 159
#> [201,] 62 17 1 10 1 180
#> [202,] 58 7 0 180 1 150
#> [203,] 63 3 1 1 0 180
#> [204,] 63 1 0 180 1 130
#> [205,] 63 4 1 3 0 222
#> [206,] 62 3 0 180 1 105
#> [207,] 63 4 0 180 1 190
#> [208,] 63 15 1 10 1 126
#> [209,] 64 4 0 180 0 130
#> [210,] 63 4 1 1 0 155
#> [211,] 60 18 1 13 0 132
#> [212,] 59 8 0 180 1 140
#> [213,] 58 2 0 180 0 127
#> [214,] 59 4 0 180 0 196
#> [215,] 60 7 1 5 1 141
#> [216,] 60 7 1 1 1 90
#> [217,] 63 1 0 1 0 162
#> [218,] 61 15 1 13 0 170
#> [219,] 59 4 0 4 0 149
#> [220,] 62 6 0 6 0 120
#> [221,] 63 12 1 10 0 200
#> [222,] 61 6 1 1 1 117
#> [223,] 64 12 1 11 0 160
#> [224,] 66 1 1 0 1 120
#> [225,] 65 36 1 11 0 140
#> [226,] 63 4 1 3 0 162
#> [227,] 61 10 1 2 1 194
#> [228,] 63 12 1 9 0 114
#> [229,] 63 7 0 180 0 120
#> [230,] 66 5 1 0 1 110
#> [231,] 65 8 1 0 0 168
#> [232,] 64 0 0 0 1 90
#> [233,] 61 12 1 11 0 154
#> [234,] 64 9 0 180 0 150
#> [235,] 61 4 0 180 1 113
#> [236,] 63 16 1 7 1 110
#> [237,] 66 6 1 1 1 130
#> [238,] 62 3 1 1 1 199
#> [239,] 65 6 0 9 0 112
#> [240,] 65 3 1 0 1 80
#> [241,] 63 5 1 4 0 170
#> [242,] 63 2 1 1 0 180
#> [243,] 62 13 1 11 0 180
#> [244,] 67 11 0 11 1 100
#> [245,] 66 18 1 5 0 142
#> [246,] 66 16 1 11 1 169
#> [247,] 62 9 0 180 0 145
#> [248,] 61 14 1 5 0 140
#> [249,] 63 9 1 8 1 160
#> [250,] 63 3 1 2 0 120
#> [251,] 63 2 1 0 0 140
#> [252,] 64 19 1 8 1 160
#> [253,] 67 6 0 180 1 170
#> [254,] 68 5 1 4 1 150
#> [255,] 64 13 1 12 1 150
#> [256,] 64 6 1 0 1 125
#> [257,] 66 7 1 0 1 115
#> [258,] 64 14 1 13 1 150
#> [259,] 64 0 0 0 1 148
#> [260,] 67 4 1 3 0 130
#> [261,] 66 3 1 0 1 135
#> [262,] 66 6 1 0 1 140
#> [263,] 65 2 1 1 1 170
#> [264,] 68 1 0 180 1 166
#> [265,] 68 5 0 5 1 90
#> [266,] 63 10 0 16 1 160
#> [267,] 66 14 0 180 0 130
#> [268,] 64 1 0 1 1 120
#> [269,] 65 17 1 14 1 100
#> [270,] 63 8 1 1 1 162
#> [271,] 63 10 0 18 1 130
#> [272,] 68 14 0 79 0 172
#> [273,] 66 12 1 10 1 150
#> [274,] 65 15 1 12 1 150
#> [275,] 69 12 0 15 1 140
#> [276,] 66 15 1 13 1 160
#> [277,] 63 2 0 180 0 150
#> [278,] 65 11 1 6 0 130
#> [279,] 69 21 1 10 0 180
#> [280,] 69 6 0 180 1 100
#> [281,] 63 8 0 180 1 120
#> [282,] 68 14 1 13 1 140
#> [283,] 65 8 1 0 1 90
#> [284,] 67 1 0 180 1 160
#> [285,] 68 10 1 10 1 150
#> [286,] 67 7 1 4 1 130
#> [287,] 63 2 1 0 0 99
#> [288,] 67 2 0 180 0 184
#> [289,] 65 6 0 6 0 80
#> [290,] 65 10 1 1 1 148
#> [291,] 66 19 1 12 1 150
#> [292,] 67 12 1 12 0 160
#> [293,] 69 6 0 99 1 140
#> [294,] 65 4 1 1 0 130
#> [295,] 64 4 0 179 0 160
#> [296,] 70 15 1 12 1 132
#> [297,] 64 4 0 180 1 140
#> [298,] 64 0 1 0 1 118
#> [299,] 67 2 0 18 0 131
#> [300,] 66 7 1 5 1 131
#> [301,] 66 4 0 180 0 177
#> [302,] 68 4 1 0 1 160
#> [303,] 69 4 1 3 1 150
#> [304,] 69 17 1 10 0 140
#> [305,] 69 8 0 93 0 140
#> [306,] 66 6 0 180 0 140
#> [307,] 65 1 0 1 1 120
#> [308,] 68 18 1 0 1 160
#> [309,] 68 4 0 4 1 190
#> [310,] 71 3 0 5 0 112
#> [311,] 68 7 0 150 0 210
#> [312,] 66 9 1 3 1 151
#> [313,] 70 4 1 0 1 180
#> [314,] 70 14 0 171 0 166
#> [315,] 67 10 1 9 0 200
#> [316,] 67 6 1 4 0 130
#> [317,] 68 18 1 14 1 170
#> [318,] 69 0 0 0 1 148
#> [319,] 65 2 0 180 0 130
#> [320,] 69 3 1 2 0 151
#> [321,] 69 8 0 180 1 180
#> [322,] 71 7 0 7 0 230
#> [323,] 66 2 0 2 1 228
#> [324,] 69 5 0 5 1 142
#> [325,] 69 3 0 3 1 130
#> [326,] 70 22 1 13 0 103
#> [327,] 68 6 0 180 0 145
#> [328,] 69 6 1 4 1 174
#> [329,] 72 3 1 0 1 132
#> [330,] 69 8 1 7 1 108
#> [331,] 67 3 0 180 0 110
#> [332,] 66 2 1 1 0 123
#> [333,] 69 19 0 180 0 130
#> [334,] 67 14 0 172 1 140
#> [335,] 67 7 1 4 0 122
#> [336,] 68 2 0 7 1 130
#> [337,] 69 8 1 2 0 121
#> [338,] 70 3 0 123 0 130
#> [339,] 70 9 0 180 1 142
#> [340,] 67 12 1 8 0 120
#> [341,] 69 1 0 1 1 110
#> [342,] 67 1 0 1 1 60
#> [343,] 69 5 0 76 0 120
#> [344,] 67 8 1 0 1 130
#> [345,] 72 13 1 11 1 195
#> [346,] 68 10 1 8 1 160
#> [347,] 66 24 1 13 0 130
#> [348,] 72 30 1 0 1 145
#> [349,] 68 7 1 2 0 135
#> [350,] 73 20 1 0 1 170
#> [351,] 71 6 0 9 0 120
#> [352,] 70 11 0 180 1 210
#> [353,] 72 19 1 8 0 120
#> [354,] 72 12 1 10 0 170
#> [355,] 67 8 0 180 1 170
#> [356,] 67 5 1 0 1 147
#> [357,] 67 9 0 180 0 158
#> [358,] 73 13 0 152 1 130
#> [359,] 70 5 0 180 0 150
#> [360,] 67 4 1 1 0 134
#> [361,] 72 6 1 5 0 115
#> [362,] 71 1 0 173 1 188
#> [363,] 68 23 0 180 1 220
#> [364,] 70 3 0 180 0 121
#> [365,] 71 3 1 2 0 150
#> [366,] 68 4 1 3 0 210
#> [367,] 72 5 0 28 0 120
#> [368,] 71 5 0 180 0 191
#> [369,] 73 6 0 180 1 117
#> [370,] 69 16 1 10 1 140
#> [371,] 69 8 1 1 0 164
#> [372,] 68 7 0 180 1 130
#> [373,] 70 4 0 180 0 180
#> [374,] 69 1 1 0 0 155
#> [375,] 72 8 1 1 1 150
#> [376,] 71 2 1 0 1 180
#> [377,] 73 7 0 7 1 140
#> [378,] 68 15 1 13 1 130
#> [379,] 70 13 1 9 0 100
#> [380,] 72 6 0 180 1 130
#> [381,] 74 8 1 0 1 85
#> [382,] 73 4 0 180 1 154
#> [383,] 69 2 1 0 1 110
#> [384,] 68 9 0 180 1 120
#> [385,] 71 20 1 10 0 140
#> [386,] 73 3 1 0 1 136
#> [387,] 70 5 1 0 1 190
#> [388,] 73 10 1 8 0 106
#> [389,] 74 4 0 4 0 120
#> [390,] 72 5 1 3 1 160
#> [391,] 70 3 0 180 1 154
#> [392,] 71 7 1 2 0 143
#> [393,] 74 3 0 3 1 150
#> [394,] 73 17 1 11 0 140
#> [395,] 71 13 1 8 0 121
#> [396,] 70 4 1 0 1 140
#> [397,] 71 14 1 13 1 170
#> [398,] 74 7 1 0 1 117
#> [399,] 72 10 1 8 1 153
#> [400,] 69 7 0 180 1 144
#> [401,] 70 8 0 8 0 120
#> [402,] 75 1 0 1 0 133
#> [403,] 75 2 1 1 0 145
#> [404,] 72 10 1 9 1 160
#> [405,] 73 10 1 10 1 120
#> [406,] 74 15 1 9 1 179
#> [407,] 71 2 0 10 1 112
#> [408,] 73 1 0 1 1 80
#> [409,] 75 13 1 1 1 130
#> [410,] 71 4 0 4 0 134
#> [411,] 72 15 1 12 1 120
#> [412,] 70 7 1 4 0 184
#> [413,] 72 1 1 1 0 168
#> [414,] 72 7 0 57 1 145
#> [415,] 73 10 0 180 0 162
#> [416,] 72 11 0 11 1 140
#> [417,] 73 5 1 3 1 112
#> [418,] 76 25 1 12 1 170
#> [419,] 73 12 1 12 1 140
#> [420,] 75 1 0 180 1 140
#> [421,] 72 4 1 0 1 197
#> [422,] 73 4 0 180 0 124
#> [423,] 76 3 1 0 1 120
#> [424,] 72 5 0 180 0 154
#> [425,] 72 3 0 180 0 160
#> [426,] 76 5 0 5 1 130
#> [427,] 77 4 0 4 0 185
#> [428,] 75 3 1 1 0 180
#> [429,] 73 15 0 15 1 160
#> [430,] 73 10 1 10 0 124
#> [431,] 74 7 0 180 1 150
#> [432,] 74 3 0 3 1 128
#> [433,] 76 1 0 180 0 114
#> [434,] 74 2 1 1 0 140
#> [435,] 76 8 1 0 1 141
#> [436,] 74 19 1 4 1 200
#> [437,] 75 23 1 14 1 110
#> [438,] 74 2 0 180 0 190
#> [439,] 72 4 0 85 1 120
#> [440,] 72 4 1 3 0 160
#> [441,] 76 17 1 0 1 200
#> [442,] 73 4 1 3 1 125
#> [443,] 76 13 1 10 0 110
#> [444,] 75 0 0 0 1 130
#> [445,] 75 4 1 2 1 188
#> [446,] 74 6 0 180 0 160
#> [447,] 76 4 0 4 1 155
#> [448,] 75 1 0 1 1 125
#> [449,] 73 1 0 52 1 105
#> [450,] 73 0 0 180 0 156
#> [451,] 72 5 0 180 0 120
#> [452,] 78 12 1 11 1 160
#> [453,] 76 44 1 10 0 105
#> [454,] 76 5 0 180 0 185
#> [455,] 74 10 1 0 1 135
#> [456,] 74 8 1 8 1 170
#> [457,] 77 5 1 0 0 123
#> [458,] 77 12 0 180 0 130
#> [459,] 77 1 1 0 1 90
#> [460,] 76 12 1 11 1 120
#> [461,] 78 5 1 0 1 170
#> [462,] 74 9 1 8 0 200
#> [463,] 75 6 0 180 0 150
#> [464,] 79 10 1 8 0 190
#> [465,] 74 2 1 0 1 130
#> [466,] 76 29 0 47 0 90
#> [467,] 73 8 1 1 1 162
#> [468,] 73 11 1 2 1 110
#> [469,] 78 8 1 6 1 110
#> [470,] 74 7 0 7 0 161
#> [471,] 78 32 1 9 1 198
#> [472,] 80 10 1 6 1 147
#> [473,] 78 0 0 180 1 212
#> [474,] 78 13 1 5 0 130
#> [475,] 75 12 1 1 1 120
#> [476,] 78 15 0 180 1 270
#> [477,] 80 8 0 8 1 120
#> [478,] 75 13 1 6 0 150
#> [479,] 74 10 1 8 0 135
#> [480,] 76 1 0 1 1 83
#> [481,] 78 12 1 9 0 150
#> [482,] 75 4 1 0 0 212
#> [483,] 77 2 1 0 1 143
#> [484,] 76 11 1 0 0 120
#> [485,] 75 11 1 4 0 162
#> [486,] 75 3 0 3 0 0
#> [487,] 76 7 0 29 1 150
#> [488,] 77 24 0 24 1 160
#> [489,] 80 9 0 23 1 128
#> [490,] 78 6 1 0 1 240
#> [491,] 76 3 1 0 1 140
#> [492,] 79 11 0 180 0 160
#> [493,] 79 2 1 0 1 121
#> [494,] 78 14 1 0 1 140
#> [495,] 81 1 0 1 0 130
#> [496,] 76 4 0 4 1 160
#> [497,] 79 4 0 4 1 125
#> [498,] 76 12 1 10 1 127
#> [499,] 77 6 0 6 1 107
#> [500,] 80 3 1 0 1 120
#> [501,] 78 11 0 180 1 135
#> [502,] 76 1 0 1 1 140
#> [503,] 77 31 1 3 1 161
#> [504,] 78 7 1 0 1 110
#> [505,] 79 3 0 3 0 120
#> [506,] 77 7 0 180 1 170
#> [507,] 77 6 0 6 1 144
#> [508,] 79 4 1 0 1 120
#> [509,] 81 1 0 180 0 120
#> [510,] 80 15 1 12 1 150
#> [511,] 77 9 1 4 0 141
#> [512,] 82 5 0 8 1 120
#> [513,] 80 40 1 0 1 138
#> [514,] 78 4 0 59 1 112
#> [515,] 80 17 1 12 0 100
#> [516,] 76 7 0 161 0 151
#> [517,] 79 10 0 10 1 120
#> [518,] 80 15 1 0 1 90
#> [519,] 81 4 1 2 1 126
#> [520,] 78 32 0 180 1 130
#> [521,] 81 3 0 180 0 184
#> [522,] 78 15 0 15 0 165
#> [523,] 80 5 1 1 1 108
#> [524,] 78 4 0 180 0 175
#> [525,] 78 26 1 5 0 194
#> [526,] 76 1 0 166 0 131
#> [527,] 81 4 1 1 1 104
#> [528,] 80 1 0 1 0 100
#> [529,] 78 3 1 1 1 152
#> [530,] 80 2 1 1 0 168
#> [531,] 79 6 0 6 0 152
#> [532,] 80 6 1 0 1 119
#> [533,] 82 1 1 0 1 82
#> [534,] 79 10 0 180 1 150
#> [535,] 77 4 0 180 1 98
#> [536,] 78 12 0 180 0 134
#> [537,] 82 21 1 2 0 155
#> [538,] 79 4 0 4 1 121
#> [539,] 80 6 0 6 1 110
#> [540,] 82 5 0 180 0 110
#> [541,] 83 5 0 180 0 148
#> [542,] 79 7 1 6 0 130
#> [543,] 83 4 0 103 0 97
#> [544,] 81 11 1 8 0 160
#> [545,] 81 5 0 177 0 41
#> [546,] 80 11 1 8 0 170
#> [547,] 78 23 1 10 1 145
#> [548,] 79 4 0 4 1 183
#> [549,] 78 9 1 4 1 120
#> [550,] 82 8 1 1 0 128
#> [551,] 79 1 0 180 1 170
#> [552,] 81 15 0 180 1 140
#> [553,] 80 7 1 0 1 146
#> [554,] 84 5 1 1 1 85
#> [555,] 81 20 1 9 0 170
#> [556,] 83 8 0 8 0 115
#> [557,] 80 6 1 0 1 150
#> [558,] 80 11 1 8 0 110
#> [559,] 81 8 0 180 0 146
#> [560,] 80 8 1 7 0 160
#> [561,] 85 4 0 180 0 90
#> [562,] 81 2 1 1 0 198
#> [563,] 83 2 0 2 1 155
#> [564,] 82 6 0 128 1 100
#> [565,] 80 3 1 1 1 230
#> [566,] 82 23 1 0 0 110
#> [567,] 84 4 0 4 1 85
#> [568,] 81 1 0 1 1 150
#> [569,] 84 1 0 38 1 205
#> [570,] 83 3 0 180 0 174
#> [571,] 81 4 0 90 1 138
#> [572,] 80 13 1 8 1 140
#> [573,] 84 4 0 89 1 129
#> [574,] 80 2 1 0 1 130
#> [575,] 79 4 0 4 1 60
#> [576,] 83 1 0 1 1 100
#> [577,] 82 19 0 19 0 120
#> [578,] 80 30 1 13 0 220
#> [579,] 83 9 0 180 0 198
#> [580,] 83 3 0 114 0 98
#> [581,] 83 2 0 154 0 130
#> [582,] 82 0 0 2 1 100
#> [583,] 83 1 0 180 0 160
#> [584,] 81 4 0 4 0 175
#> [585,] 84 15 1 13 1 110
#> [586,] 81 1 0 1 1 145
#> [587,] 81 12 0 12 1 163
#> [588,] 82 5 1 0 1 146
#> [589,] 81 4 0 4 0 160
#> [590,] 86 12 0 180 1 120
#> [591,] 81 19 1 14 0 120
#> [592,] 82 3 1 2 0 130
#> [593,] 82 15 1 0 0 183
#> [594,] 80 2 0 88 0 135
#> [595,] 83 7 0 126 0 135
#> [596,] 84 6 0 165 0 145
#> [597,] 86 3 0 3 1 140
#> [598,] 84 3 0 180 1 120
#> [599,] 81 13 0 180 0 152
#> [600,] 81 2 1 0 1 118
#> [601,] 81 4 0 180 0 160
#> [602,] 83 9 0 180 1 149
#> [603,] 82 1 0 180 1 193
#> [604,] 83 4 0 4 0 130
#> [605,] 86 12 1 0 1 132
#> [606,] 82 14 1 11 1 103
#> [607,] 86 6 1 0 1 140
#> [608,] 83 19 0 43 0 150
#> [609,] 84 3 1 2 0 125
#> [610,] 88 14 1 3 1 130
#> [611,] 84 3 0 3 1 121
#> [612,] 84 7 1 2 0 148
#> [613,] 87 2 0 180 0 113
#> [614,] 84 9 0 92 1 110
#> [615,] 84 3 0 180 1 170
#> [616,] 86 4 0 38 1 122
#> [617,] 82 4 0 4 0 130
#> [618,] 86 13 0 177 0 163
#> [619,] 85 3 0 3 1 113
#> [620,] 86 6 0 6 1 117
#> [621,] 88 4 0 4 1 115
#> [622,] 83 9 0 65 1 150
#> [623,] 86 9 1 7 1 142
#> [624,] 88 3 0 115 0 110
#> [625,] 83 3 0 3 1 130
#> [626,] 86 15 1 8 1 109
#> [627,] 89 4 0 4 1 153
#> [628,] 89 5 0 119 1 140
#> [629,] 87 6 0 180 1 110
#> [630,] 87 1 0 1 0 170
#> [631,] 84 8 0 180 1 119
#> [632,] 84 2 0 110 1 174
#> [633,] 87 29 0 29 1 97
#> [634,] 87 15 1 9 1 138
#> [635,] 84 0 0 180 1 136
#> [636,] 90 14 0 14 1 100
#> [637,] 86 4 0 180 1 145
#> [638,] 87 2 0 180 0 160
#> [639,] 91 10 0 145 0 135
#> [640,] 88 7 0 24 0 119
#> [641,] 88 8 0 50 1 154
#> [642,] 90 11 1 10 1 186
#> [643,] 87 6 0 126 1 168
#> [644,] 86 9 1 7 0 130
#> [645,] 91 1 0 1 1 74
#> [646,] 87 43 0 178 1 130
#> [647,] 87 5 0 36 1 150
#> [648,] 88 3 1 2 0 159
#> [649,] 91 3 0 33 1 137
#> [650,] 87 7 0 74 1 105
#> [651,] 89 12 1 0 1 130
#> [652,] 91 5 0 169 1 176
#> [653,] 89 52 0 52 1 130
#> [654,] 89 4 0 4 1 159
#> [655,] 91 0 0 0 0 0
#> [656,] 90 19 1 11 1 129
#> [657,] 90 1 0 1 1 118
#> [658,] 93 8 0 179 1 110
#> [659,] 92 4 0 76 1 149
#> [660,] 91 1 0 180 0 158
#> [661,] 90 16 0 16 1 106
#> [662,] 90 3 0 67 0 162
#> [663,] 96 3 0 12 1 97
#> [664,] 95 8 1 5 1 150
#> [665,] 91 7 0 7 0 135
#> [666,] 93 0 1 0 1 122
#> [667,] 92 2 0 2 0 112
#> [668,] 93 4 0 180 1 135
#> [669,] 96 3 1 0 1 104
#> [670,] 96 15 1 0 1 140
#>
#> $y
#> [1] 5.0+ 2.0+ 5.0+ 180.0+ 5.0+ 180.0+ 2.0+ 180.0+ 180.0+ 115.0
#> [11] 180.0+ 12.0 5.0+ 180.0+ 180.0+ 180.0+ 5.0+ 2.0+ 180.0+ 3.0
#> [21] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 2.0+ 155.0+ 180.0+ 180.0+ 180.0+
#> [31] 180.0+ 180.0+ 180.0+ 180.0+ 150.0 180.0+ 180.0+ 180.0+ 180.0+ 73.0
#> [41] 5.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 177.0+ 180.0+ 180.0+
#> [51] 180.0+ 180.0+ 180.0+ 180.0+ 10.0+ 172.0+ 180.0+ 180.0+ 180.0+ 7.0
#> [61] 180.0+ 2.0 1.0 179.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [71] 180.0+ 180.0+ 7.0 36.0 88.0+ 180.0+ 4.0+ 180.0+ 180.0+ 180.0+
#> [81] 180.0+ 180.0+ 180.0+ 5.0 180.0+ 180.0+ 180.0+ 180.0+ 4.0+ 85.0
#> [91] 180.0+ 166.0+ 180.0+ 16.0+ 152.0+ 7.0+ 6.0+ 180.0+ 180.0+ 180.0+
#> [101] 13.0+ 171.0+ 180.0+ 174.0+ 1.0 180.0+ 9.0+ 180.0+ 180.0+ 180.0+
#> [111] 175.0+ 180.0+ 7.0+ 180.0+ 180.0+ 180.0+ 180.0+ 16.0+ 16.0 180.0+
#> [121] 180.0+ 180.0+ 12.0+ 134.0+ 2.0 3.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [131] 180.0+ 180.0+ 8.0+ 180.0+ 0.5 180.0+ 180.0+ 180.0+ 15.0 5.0+
#> [141] 180.0+ 4.0+ 147.0+ 180.0+ 180.0+ 5.0+ 180.0+ 4.0+ 180.0+ 9.0+
#> [151] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 2.0 180.0+ 180.0+ 180.0+
#> [161] 180.0+ 180.0+ 180.0+ 0.5 180.0+ 161.0+ 171.0+ 180.0+ 180.0+ 1.0
#> [171] 180.0+ 180.0+ 180.0+ 17.0 45.0 3.0+ 180.0+ 180.0+ 172.0+ 8.0
#> [181] 1.0+ 15.0 180.0+ 180.0+ 180.0+ 77.0 180.0+ 180.0+ 7.0+ 180.0+
#> [191] 180.0+ 180.0+ 3.0+ 180.0+ 28.0 13.0+ 5.0 18.0 180.0+ 180.0+
#> [201] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 4.0+
#> [211] 180.0+ 180.0+ 180.0+ 180.0+ 84.0 180.0+ 1.0 180.0+ 4.0+ 6.0+
#> [221] 180.0+ 180.0+ 12.0 180.0+ 36.0 180.0+ 88.0 12.0 180.0+ 180.0+
#> [231] 180.0+ 0.5 12.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 9.0 3.0
#> [241] 180.0+ 180.0+ 180.0+ 11.0+ 18.0+ 180.0+ 180.0+ 180.0+ 180.0+ 3.0+
#> [251] 2.0+ 103.0 180.0+ 5.0+ 13.0 180.0+ 179.0+ 14.0+ 0.5+ 180.0+
#> [261] 3.0+ 180.0+ 175.0+ 180.0+ 5.0 16.0 180.0+ 1.0 180.0+ 180.0+
#> [271] 18.0 79.0 80.0 15.0+ 15.0 180.0+ 180.0+ 180.0+ 174.0+ 180.0+
#> [281] 180.0+ 180.0+ 8.0+ 180.0+ 10.0 180.0+ 180.0+ 180.0+ 6.0 180.0+
#> [291] 19.0+ 12.0 99.0 180.0+ 179.0+ 180.0+ 180.0+ 0.5 18.0 7.0+
#> [301] 180.0+ 180.0+ 152.0+ 180.0+ 93.0 180.0+ 1.0 18.0+ 4.0 5.0
#> [311] 150.0 180.0+ 180.0+ 171.0 174.0+ 6.0 180.0+ 0.5 180.0+ 180.0+
#> [321] 180.0+ 7.0+ 2.0 5.0+ 3.0+ 180.0+ 180.0+ 97.0 180.0+ 8.0+
#> [331] 180.0+ 2.0+ 180.0+ 172.0+ 7.0 7.0 8.0+ 123.0 180.0+ 180.0+
#> [341] 1.0 1.0 76.0 180.0+ 132.0 10.0+ 180.0+ 162.0 7.0+ 124.0
#> [351] 9.0 180.0+ 180.0+ 12.0 180.0+ 180.0+ 180.0+ 152.0 180.0+ 76.0
#> [361] 180.0+ 173.0+ 180.0+ 180.0+ 180.0+ 180.0+ 28.0 180.0+ 180.0+ 16.0+
#> [371] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 7.0+ 15.0 13.0+ 180.0+
#> [381] 180.0+ 180.0+ 2.0 180.0+ 20.0 180.0+ 180.0+ 87.0 4.0+ 180.0+
#> [391] 180.0+ 180.0+ 3.0 180.0+ 175.0 180.0+ 14.0+ 180.0+ 10.0+ 180.0+
#> [401] 8.0+ 1.0 180.0+ 159.0 15.0 180.0+ 10.0 1.0 13.0 4.0+
#> [411] 180.0+ 104.0+ 1.0 57.0 180.0+ 11.0 5.0 180.0+ 12.0 180.0+
#> [421] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 5.0 4.0+ 180.0+ 15.0+ 10.0
#> [431] 180.0+ 3.0 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 85.0 180.0+
#> [441] 17.0+ 180.0+ 174.0+ 0.5 46.0 180.0+ 4.0 1.0 52.0 180.0+
#> [451] 180.0+ 12.0 180.0+ 180.0+ 180.0+ 8.0 5.0 180.0+ 1.0 12.0
#> [461] 180.0+ 168.0+ 180.0+ 180.0+ 176.0+ 47.0 180.0+ 11.0 8.0+ 7.0
#> [471] 32.0 10.0 180.0+ 172.0 12.0 180.0+ 8.0 180.0+ 180.0+ 1.0
#> [481] 180.0+ 4.0+ 2.0 11.0 152.0+ 3.0 29.0 24.0 23.0 180.0+
#> [491] 3.0+ 180.0+ 180.0+ 180.0+ 1.0 4.0 4.0 180.0+ 6.0 3.0+
#> [501] 180.0+ 1.0 171.0 43.0 3.0 180.0+ 6.0 138.0 180.0+ 180.0+
#> [511] 71.0 8.0 40.0 59.0 17.0 161.0 10.0+ 180.0+ 93.0 180.0+
#> [521] 180.0+ 15.0+ 5.0+ 180.0+ 171.0+ 166.0+ 71.0 1.0 3.0+ 10.0
#> [531] 6.0+ 6.0 1.0 180.0+ 180.0+ 180.0+ 180.0+ 4.0 6.0 180.0+
#> [541] 180.0+ 180.0+ 103.0 180.0+ 177.0+ 169.0 70.0 4.0 180.0+ 180.0+
#> [551] 180.0+ 180.0+ 7.0+ 180.0+ 20.0 8.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [561] 180.0+ 180.0+ 2.0 128.0 3.0+ 62.0 4.0 1.0 38.0 180.0+
#> [571] 90.0 180.0+ 89.0 180.0+ 4.0 1.0 19.0 30.0 180.0+ 114.0
#> [581] 154.0 2.0 180.0+ 4.0+ 180.0+ 1.0 12.0 5.0+ 4.0+ 180.0+
#> [591] 180.0+ 3.0 83.0 88.0 126.0 165.0 3.0 180.0+ 180.0+ 180.0+
#> [601] 180.0+ 180.0+ 180.0+ 4.0+ 180.0+ 174.0 6.0 43.0 180.0+ 14.0
#> [611] 3.0 180.0+ 180.0+ 92.0 180.0+ 38.0 4.0 177.0 3.0+ 6.0+
#> [621] 4.0 65.0 11.0 115.0 3.0+ 180.0+ 4.0 119.0 180.0+ 1.0+
#> [631] 180.0+ 110.0 29.0 180.0+ 180.0+ 14.0 180.0+ 180.0+ 145.0 24.0
#> [641] 50.0 11.0 126.0 180.0+ 1.0 178.0+ 36.0 75.0 33.0 74.0
#> [651] 180.0+ 169.0 52.0 4.0 0.5 180.0+ 1.0+ 179.0+ 76.0 180.0+
#> [661] 16.0 67.0 12.0 8.0 7.0+ 0.5 2.0 180.0+ 3.0 15.0+
#>
#> $weights
#> NULL
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
#> Warning: Multiple lambdas have been fit. Lambda will be set to 0.01 (see parameter 's').
# Score the predictions
predictions$score()
#> surv.cindex
#> 0.8279966