Classification Learning Vector Quantization 1
Source:R/learner_class_classif_lvq1.R
mlr_learners_classif.lvq1.RdLearning Vector Quantization 1.
Calls class::lvqinit(), class::lvq1(), and class::lvqtest() from class.
Meta Information
Task type: “classif”
Predict Types: “response”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3extralearners, class
Parameters
| Id | Type | Default | Range |
| alpha | numeric | 0.03 | \([0, \infty)\) |
| k | integer | 5 | \([1, \infty)\) |
| niter | integer | NULL | \([1, \infty)\) |
| prior | untyped | NULL | - |
| size | integer | NULL | \([1, \infty)\) |
References
Kohonen, Teuvo (1990). “The self-organizing map.” Proceedings of the IEEE, 78(9), 1464–1480. doi:10.1109/5.58325 .
Kohonen, Teuvo (1995). Self-Organizing Maps, volume 30 series Springer Series in Information Sciences. Springer, Berlin. ISBN 978-3540967120.
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/chapters/chapter2/data_and_basic_modeling.html#sec-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 -> mlr3::LearnerClassif -> LearnerClassifLvq1
Methods
Inherited methods
mlr3::Learner$base_learner()mlr3::Learner$configure()mlr3::Learner$encapsulate()mlr3::Learner$format()mlr3::Learner$help()mlr3::Learner$predict()mlr3::Learner$predict_newdata()mlr3::Learner$print()mlr3::Learner$reset()mlr3::Learner$selected_features()mlr3::Learner$train()mlr3::LearnerClassif$predict_newdata_fast()
Examples
# Define the Learner
learner = lrn("classif.lvq1")
print(learner)
#>
#> ── <LearnerClassifLvq1> (classif.lvq1): Learning Vector Quantization 1 ─────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3extralearners, and class
#> • Predict Types: [response]
#> • Feature Types: integer and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: multiclass and twoclass
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'
# Define a Task
task = tsk("sonar")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
print(learner$model)
#> $x
#> V1 V10 V11 V12 V13 V14
#> [1,] 0.032300000 0.21540000 0.308500000 0.34250000 0.29900000 0.14020000
#> [2,] 0.026586315 0.17766452 0.320573047 0.46961461 0.55230060 0.53582678
#> [3,] 0.051346200 -0.13529371 -0.002971205 0.10434296 0.13672851 0.25039661
#> [4,] 0.027111001 0.31660908 0.390053152 0.42533404 0.41791919 0.44550767
#> [5,] 0.034057357 0.36766573 0.435168545 0.43052679 0.47943540 0.40142336
#> [6,] 0.069118581 0.13421549 0.303473880 0.47692669 0.53458611 0.58616709
#> [7,] 0.040836194 0.27208224 0.294011334 0.26140567 0.21186782 0.11274125
#> [8,] 0.040847559 0.18126716 0.257447421 0.28120647 0.25882860 0.24929794
#> [9,] 0.032793724 0.62837684 0.655241254 0.46438048 0.29545905 0.18669733
#> [10,] 0.070838154 0.58901918 0.439751417 0.17887806 0.19460167 0.35565527
#> [11,] 0.018273096 0.45042316 0.419240883 0.38414893 0.47041942 0.61115623
#> [12,] 0.016358874 0.32529143 0.288856365 0.30702333 0.24963622 0.17785670
#> [13,] 0.025682289 0.25193902 0.294975925 0.34187586 0.36152515 0.41017314
#> [14,] 0.055816301 0.26221160 0.259736575 0.10054808 0.14086093 0.46455084
#> [15,] 0.024566130 0.08962105 0.248392348 0.37235444 0.30049516 0.27753124
#> [16,] 0.020076141 0.13869644 0.123096724 0.08794871 0.06117716 0.14610554
#> [17,] 0.024568011 0.01390898 -0.001885687 0.07322521 0.16031688 0.20538872
#> [18,] 0.011497028 0.11367612 0.123766936 0.10788534 0.06504034 0.11576766
#> [19,] 0.022302869 0.12278959 0.122167824 0.13798926 0.12577790 0.12255871
#> [20,] 0.004135430 0.03940966 0.048801908 0.09795050 0.14422965 0.12787909
#> [21,] 0.009936376 0.19549558 0.145547100 0.11193817 0.21827903 0.30373031
#> [22,] 0.012217184 0.03581766 0.121522160 0.10455560 0.18408457 0.24465313
#> [23,] 0.012671257 0.09625964 0.121469427 0.15769555 0.20735499 0.20790177
#> [24,] 0.013247862 0.18387140 0.131139284 0.06666421 0.07119836 0.08614889
#> [25,] 0.014160517 0.06963358 0.056328006 0.08944793 0.22640860 0.27325122
#> V15 V16 V17 V18 V19 V2
#> [1,] 0.12350000 0.15340000 0.19010000 0.2429000 0.2120000 0.010100000
#> [2,] 0.60455662 0.69436368 0.89106537 0.9870491 0.9908444 0.039916330
#> [3,] 0.38951745 0.53144775 0.74971416 0.7572452 0.8730498 0.046821829
#> [4,] 0.49340346 0.53685087 0.65752176 0.7123321 0.7321964 0.011533599
#> [5,] 0.25088458 0.31502107 0.34395904 0.3177599 0.4843924 0.044190021
#> [6,] 0.70751732 0.71379006 0.83964752 0.9016819 0.9911965 0.063233519
#> [7,] 0.10789526 0.17111926 0.19864745 0.2497139 0.2057916 0.040596936
#> [8,] 0.25393996 0.24609622 0.27151118 0.2444667 0.3120988 0.061901481
#> [9,] 0.21744496 0.30081327 0.26194928 0.3231537 0.4245217 0.061952851
#> [10,] 0.45235963 0.45109613 0.24908548 0.2553180 0.4248363 0.081355132
#> [11,] 0.56030388 0.65028555 0.80170695 0.8137300 0.8257388 0.099657280
#> [12,] 0.14893000 0.19474796 0.19407362 0.2185647 0.1717460 0.023427371
#> [13,] 0.44994906 0.48537427 0.53851203 0.6221572 0.6940384 0.019081314
#> [14,] 0.66743602 0.70510718 0.75891345 0.6334569 0.4418460 0.037369231
#> [15,] 0.30448578 0.46005689 0.61316395 0.5278118 0.4817753 0.056085953
#> [16,] 0.06649431 0.08968130 0.25609852 0.2627696 0.3074446 0.005879836
#> [17,] 0.24184300 0.22474241 0.29542616 0.3026061 0.3253458 0.027873938
#> [18,] 0.09762906 0.08052322 0.18838324 0.2926830 0.3045453 0.013682234
#> [19,] 0.14216127 0.21076539 0.27378140 0.3243834 0.3679750 0.030741749
#> [20,] 0.07117526 0.09143053 0.12215852 0.1432595 0.1176236 0.009321573
#> [21,] 0.37484860 0.27026664 0.09660639 0.1500333 0.2269362 0.021045048
#> [22,] 0.30211730 0.26266672 0.27699388 0.3593766 0.4997925 -0.002729681
#> [23,] 0.24938045 0.27036923 0.26731815 0.2158095 0.1734691 0.013866408
#> [24,] 0.13044085 0.22784520 0.27209712 0.2635819 0.3776779 0.001466814
#> [25,] 0.22494938 0.23342307 0.20001132 0.3509247 0.6391463 0.030552262
#> V20 V21 V22 V23 V24 V25 V26
#> [1,] 0.2395000 0.32720000 0.5949000 0.8302000 0.9045000 0.9888000 0.99120000
#> [2,] 0.9283041 0.87341975 0.7323850 0.5619629 0.4891617 0.3259069 0.05991938
#> [3,] 0.8888756 0.78976192 0.8546427 0.9664646 0.8843353 0.6358515 0.49792719
#> [4,] 0.7896949 0.87209119 0.9183287 0.9489792 1.0230501 1.0195733 1.01976384
#> [5,] 0.7018598 0.75986269 0.7829647 0.7553309 0.7458583 0.7577768 0.91806003
#> [6,] 0.9933448 0.92464997 0.8575798 0.9577936 0.9507890 0.5968397 0.47741293
#> [7,] 0.1576995 0.33403519 0.6074964 0.7707378 0.8403024 0.9143393 0.95998109
#> [8,] 0.3958173 0.47570248 0.4765487 0.5320203 0.6004308 0.6117365 0.62052065
#> [9,] 0.5490342 0.68037844 0.8433251 0.9613222 1.0009926 0.7528225 0.60899277
#> [10,] 0.8908468 0.95809271 0.3741964 0.1294865 0.1213555 0.1730979 0.34743825
#> [11,] 0.8437289 0.78736685 0.8615777 1.0615478 1.0960851 0.9777353 0.98300263
#> [12,] 0.1254876 0.09350731 0.2221118 0.4292791 0.5805621 0.7016011 0.82290642
#> [13,] 0.7531207 0.79261210 0.8963309 0.9597081 0.9927088 0.9745305 0.95558676
#> [14,] 0.2943530 0.50424535 0.7461366 0.8481207 0.9480379 0.8814788 0.65143162
#> [15,] 0.6254918 0.40487704 0.2376617 0.3955023 0.8986028 0.9423491 0.55303852
#> [16,] 0.4646897 0.59621453 0.7436736 0.8952458 0.9193117 0.9038806 0.96729608
#> [17,] 0.4815097 0.54919802 0.6453780 0.7178400 0.7767488 0.8605434 0.85396661
#> [18,] 0.5251939 0.65525984 0.7700689 0.8977222 0.8829149 0.9420212 1.00099961
#> [19,] 0.3926765 0.43023774 0.4352280 0.4395478 0.4440525 0.3824557 0.48979959
#> [20,] 0.1037547 0.17663243 0.3696817 0.4235117 0.5609453 0.6744710 0.76789736
#> [21,] 0.2576866 0.25390462 0.3468553 0.5584528 0.7261290 0.6874743 0.70792665
#> [22,] 0.5376958 0.50616720 0.5594992 0.6485042 0.6825458 0.8431448 0.92379950
#> [23,] 0.2315189 0.35897571 0.4190998 0.5329850 0.7041460 0.8654512 0.86396556
#> [24,] 0.5713096 0.66902405 0.8874311 1.0328213 0.8925587 0.8197936 0.89882847
#> [25,] 0.8283988 0.79715006 0.7531769 0.4714715 0.3917221 0.5823053 0.76363398
#> V27 V28 V29 V3 V30 V31
#> [1,] 0.94480000 1.0000000 0.90920000 0.029800000 0.7412000 0.7691000
#> [2,] 0.06021904 0.1139541 0.06481159 0.042097447 0.1014941 0.2137735
#> [3,] 0.54065063 0.4997466 0.55779613 0.102754542 0.5386409 0.2833174
#> [4,] 1.00104738 0.9875022 0.86247281 0.028466103 0.6640928 0.4808130
#> [5,] 0.96626501 0.8863379 0.73121908 0.045860945 0.5453186 0.4610812
#> [6,] 0.34645288 0.3840490 0.28962047 0.098256972 0.1868702 0.1625565
#> [7,] 0.96748833 1.0000000 0.84384135 0.021618307 0.6607201 0.7169199
#> [8,] 0.75095723 0.8397883 0.81347003 0.063588799 0.7783035 0.7054209
#> [9,] 0.47992979 0.4003231 0.40034956 0.065057823 0.4283455 0.4605573
#> [10,] 0.23274531 0.5106298 0.57065514 0.063755475 0.7533398 0.3363168
#> [11,] 0.94263952 0.6890521 0.59641343 0.109936210 0.4965509 0.2612153
#> [12,] 0.92241194 0.9457143 0.90497995 0.022737658 0.9348804 0.9169108
#> [13,] 0.96629544 0.9520776 0.82609735 0.035447198 0.5881480 0.3865785
#> [14,] 0.64216533 0.6185807 0.48129246 0.057411961 0.5496227 0.4429748
#> [15,] 0.40123159 0.7255828 0.85201568 0.056351403 0.5142580 0.5681226
#> [16,] 1.00012647 0.9215612 0.87472471 0.009677236 0.7559424 0.6903644
#> [17,] 0.97370636 0.9176084 0.68571656 0.025172329 0.5918270 0.5546146
#> [18,] 0.95409707 0.8476277 0.75563385 0.016830890 0.6712399 0.6216380
#> [19,] 0.61210464 0.7212885 0.81743258 0.035070026 0.8237932 0.7448050
#> [20,] 0.77949511 0.8485865 0.94868527 0.014209974 0.9005577 0.7464895
#> [21,] 0.72308432 0.8563644 0.96563048 0.040770928 0.8926742 0.7867488
#> [22,] 0.94491682 0.8937446 0.74167581 0.011340455 0.7204979 0.6528012
#> [23,] 0.75102480 0.7209481 0.65810700 0.014483178 0.4707689 0.3580064
#> [24,] 0.91892938 0.9055768 0.82355439 -0.001936563 0.7146394 0.6209703
#> [25,] 0.92403664 0.8469828 0.62674883 0.019668578 0.4989497 0.4395173
#> V32 V33 V34 V35 V36 V37
#> [1,] 0.7117000 0.53040000 0.21310000 0.09280000 0.1297000 0.1159000
#> [2,] 0.3649741 0.38126797 0.47668016 0.52680227 0.2401062 0.1612717
#> [3,] 0.1890082 0.27541924 0.30058477 0.38547599 0.2922520 0.3403186
#> [4,] 0.3695843 0.20133355 0.04486949 -0.01918215 0.1111991 0.2551301
#> [5,] 0.5828405 0.52056406 0.33720412 0.09813004 0.2082654 0.1929662
#> [6,] 0.2384965 0.31290349 0.26607209 0.26853861 0.2366678 0.2999720
#> [7,] 0.6759607 0.48873251 0.17662978 0.10441594 0.1129608 0.1144183
#> [8,] 0.5855239 0.44228031 0.41831461 0.31618445 0.1976456 0.2456243
#> [9,] 0.2279052 0.49060925 0.65718524 0.58808090 0.4185480 0.3650065
#> [10,] 0.3491346 0.61118637 0.80669854 0.85547928 0.8304714 0.7878040
#> [11,] 0.1673111 0.06073645 0.27494816 0.38832635 0.2908608 0.2331819
#> [12,] 0.8430837 0.72839878 0.65570292 0.65689915 0.6976731 0.6755562
#> [13,] 0.3260896 0.17501525 -0.02279994 0.10402272 0.2120868 0.2908849
#> [14,] 0.2079418 0.23210308 0.13953021 0.47236751 0.7062991 0.7162306
#> [15,] 0.7027901 0.61656416 0.90284302 0.83658193 0.7687150 0.8024606
#> [16,] 0.6516177 0.60076508 0.62864881 0.64432365 0.5533518 0.4855003
#> [17,] 0.4550488 0.29171465 0.11132597 0.11656077 0.2463096 0.2526375
#> [18,] 0.5765046 0.57153315 0.56076754 0.55608188 0.5242426 0.4642414
#> [19,] 0.5959952 0.63504023 0.68822682 0.67158201 0.7049126 0.6474531
#> [20,] 0.7357003 0.79130064 0.61943487 0.53241628 0.4950828 0.4315205
#> [21,] 0.6513248 0.61104355 0.53485183 0.41878787 0.3848926 0.2238261
#> [22,] 0.4505392 0.34661911 0.16173422 0.11510478 0.3510589 0.3311466
#> [23,] 0.2380953 0.17444926 0.20113362 0.24659451 0.2324069 0.1566377
#> [24,] 0.5253869 0.49821316 0.55422596 0.58351257 0.5118182 0.4956544
#> [25,] 0.2940036 0.31237514 0.27247468 0.27845960 0.3183862 0.4351107
#> V38 V39 V4 V40 V41 V42
#> [1,] 0.1226000 0.17680000 0.056400000 0.03450000 0.15620000 0.08240000
#> [2,] 0.3617501 0.32446315 0.086392102 0.11119752 0.03044537 0.13802002
#> [3,] 0.5363829 0.42370444 0.110874196 0.39301147 0.38867078 0.47811857
#> [4,] 0.2367622 0.19193064 0.098591644 0.24679436 0.23305392 0.31282351
#> [5,] 0.3679634 0.59667365 0.040544410 0.45885944 0.28876705 0.43211884
#> [6,] 0.4472210 0.40651461 0.154849046 0.24109244 0.12941484 0.24317003
#> [7,] 0.1497927 0.18839516 0.034346659 0.06521181 0.14693760 0.05772002
#> [8,] 0.2926941 0.29853099 0.074138164 0.28709573 0.44757145 0.49341997
#> [9,] 0.3433826 0.41463385 0.067568599 0.41301391 0.28383563 0.35397369
#> [10,] 0.8826331 0.75341249 0.119075246 0.54302507 0.40524426 0.34208390
#> [11,] 0.2719913 0.25746593 0.057628882 0.23846272 0.37302752 0.31616838
#> [12,] 0.5538763 0.46712033 0.051164662 0.49360407 0.48213524 0.42902577
#> [13,] 0.3579431 0.32308629 0.052164942 0.32278765 0.37207325 0.35642775
#> [14,] 0.3641846 0.12356318 0.040868508 0.35280009 0.44298626 0.39520898
#> [15,] 0.5133222 0.36296704 0.078268935 0.33775332 0.26401660 0.25665477
#> [16,] 0.4032661 0.22498918 0.016230897 0.17190951 0.17182258 0.05987033
#> [17,] 0.3224221 0.25739143 -0.002676565 0.16147619 0.13931359 0.21959250
#> [18,] 0.3172754 0.08043455 0.043839338 0.14914807 0.17612271 0.13445396
#> [19,] 0.5665861 0.47593659 0.033547224 0.41462709 0.33750146 0.26354243
#> [20,] 0.4477835 0.44236304 0.032345110 0.36186603 0.18729252 0.04853951
#> [21,] 0.1469897 0.17566798 0.034455747 0.11736493 0.14450563 0.19211492
#> [22,] 0.2997204 0.29126677 0.012280218 0.14020607 0.13594859 0.28215517
#> [23,] 0.1320871 0.13602097 0.018826382 0.11993789 0.17699141 0.17252955
#> [24,] 0.3401871 0.18481725 0.028289923 0.07290179 0.08035132 0.01386521
#> [25,] 0.4654722 0.41637490 0.031482551 0.27675059 0.18012075 0.14564106
#> V43 V44 V45 V46 V47 V48
#> [1,] 0.11490000 0.16940000 0.09540000 0.00800000 0.07900000 0.12550000
#> [2,] 0.17030617 0.00892535 0.08175802 0.06390818 0.03860204 0.04172654
#> [3,] 0.52934884 0.36389897 0.17786905 0.12691663 0.13828231 0.08170567
#> [4,] 0.32849847 0.16645672 0.07390141 0.09921450 0.16092537 0.13128527
#> [5,] 0.44931442 0.46546044 0.50567763 0.30233383 0.18009625 0.18169956
#> [6,] 0.35206373 0.15562714 0.03562916 0.03228221 -0.01215974 0.01926295
#> [7,] 0.08735082 0.13073162 0.08681454 0.08299978 0.11258580 0.10912462
#> [8,] 0.38695811 0.27291703 0.38831202 0.36922499 0.26874979 0.17928605
#> [9,] 0.34650166 0.17091785 0.14338875 0.05089442 0.11547490 0.17618852
#> [10,] 0.27081468 0.28080803 0.41901930 0.32511603 0.14093969 0.17054153
#> [11,] 0.03748056 0.15382410 0.18684970 0.17467532 0.10589641 0.08127225
#> [12,] 0.44530657 0.52023101 0.48343313 0.34205573 0.24220701 0.19455640
#> [13,] 0.26540327 0.22830131 0.15883531 0.13291022 0.12745912 0.09689281
#> [14,] 0.26451232 0.20112898 0.22930873 0.22248784 0.20654498 0.18091349
#> [15,] 0.04861788 0.15776146 0.15359970 0.16544310 0.11940773 0.06871272
#> [16,] 0.05804724 0.03536575 0.01511119 0.01895721 0.01950506 0.03648189
#> [17,] 0.18215548 0.16454503 0.10220645 0.01552715 0.08127357 0.08746395
#> [18,] 0.08994087 0.03233580 0.05430269 0.02319887 0.01590187 0.02320294
#> [19,] 0.24620066 0.22900132 0.19208181 0.13871123 0.08996965 0.05962680
#> [20,] 0.08403331 0.13126278 0.14989679 0.06196286 0.03165118 0.04797659
#> [21,] 0.25598641 0.16468333 0.03483433 0.03922778 0.04400635 0.03794520
#> [22,] 0.28622032 0.18155917 0.15304425 0.11261269 0.06518108 0.05412392
#> [23,] 0.17930607 0.14458981 0.09030876 0.07154970 0.03825668 0.03531884
#> [24,] 0.07389619 0.01590677 -0.03010532 0.01995685 0.03100196 0.03237792
#> [25,] 0.06835569 0.03750543 0.08038950 0.07694110 0.07200538 0.04714826
#> V49 V5 V50 V51 V52 V53
#> [1,] 0.064700000 0.076000000 0.017900000 0.0051000000 0.006100000 0.009300000
#> [2,] 0.035754301 0.110151495 0.022739306 0.0210978007 0.020071374 0.014937897
#> [3,] 0.042829882 0.141414654 0.008422641 0.0081716502 0.001505123 0.018509096
#> [4,] 0.052680635 0.157801832 0.020709811 0.0141068102 0.004311290 0.009937335
#> [5,] 0.108541656 0.097098976 0.034443914 0.0320261826 0.014527373 0.010702307
#> [6,] 0.028099060 0.243895593 0.002389800 0.0094973477 0.019530045 0.010931749
#> [7,] 0.062575440 0.047104971 0.024584709 0.0164768445 0.013990105 0.004607414
#> [8,] 0.108126210 0.057188343 0.033804877 0.0281163834 0.022317493 0.012562669
#> [9,] 0.089193325 0.026680938 0.019129358 0.0181978618 0.015728772 0.015284532
#> [10,] 0.086074126 0.080523723 0.036520843 0.0273087047 0.022899962 0.023475004
#> [11,] 0.030936531 0.141073655 0.019276398 0.0210698469 0.019293546 0.024598069
#> [12,] 0.106264104 0.078827092 0.030321223 0.0174897466 0.014361436 0.010043175
#> [13,] 0.065551066 0.091333284 0.025533121 0.0171902003 0.020010600 0.016978110
#> [14,] 0.120780914 0.041285023 0.030178346 0.0162082790 0.013357111 0.009745529
#> [15,] 0.037894232 0.133616375 0.008414709 0.0180140932 0.006582139 0.014142740
#> [16,] 0.010253602 0.049881198 0.007530914 0.0051153495 0.003920885 0.011308983
#> [17,] 0.029941481 0.008747717 0.008458196 0.0035471258 0.004284324 0.008977037
#> [18,] 0.005309399 0.073056308 0.008108475 0.0070356759 0.005867533 0.005390214
#> [19,] 0.037446794 0.050855301 0.016621859 0.0080798928 0.010704022 0.011754378
#> [20,] 0.020136623 0.030553163 0.005908752 0.0058790314 0.006114650 0.013123889
#> [21,] 0.015200028 0.030245900 0.018361873 0.0174652387 0.015754665 0.014564698
#> [22,] 0.024583118 0.032250024 0.032035202 0.0085221937 0.003291120 0.002231478
#> [23,] 0.023247878 0.030205467 0.009359694 0.0084007494 0.011444804 0.006239945
#> [24,] 0.017704753 0.059305953 0.008230163 0.0007416393 0.002862262 0.008871888
#> [25,] 0.015665573 0.028268348 0.020470234 0.0188884073 0.013812444 0.023515579
#> V54 V55 V56 V57 V58
#> [1,] 0.013500000 0.0063000000 0.006300000 0.003400000 0.0032000000
#> [2,] 0.023115865 0.0235118287 0.007185910 0.001411520 0.0165116553
#> [3,] 0.022488371 0.0007334266 0.018984100 0.008024245 0.0050115981
#> [4,] 0.018546517 -0.0007772109 0.015594105 0.003810491 0.0284451637
#> [5,] 0.012277950 0.0166552683 0.010207438 0.008925677 0.0105762694
#> [6,] 0.020258194 0.0280097097 0.025358772 0.012572600 0.0154421195
#> [7,] 0.008152840 0.0053815246 0.009245307 0.011093995 0.0119338652
#> [8,] 0.012136582 0.0139517771 0.009259841 0.008554618 0.0114247313
#> [9,] 0.017598571 0.0122796462 0.005127738 0.002149366 0.0141812102
#> [10,] 0.026461051 0.0183766767 0.021366996 0.015719207 0.0117511791
#> [11,] 0.031432199 0.0214815237 0.013522905 0.014002310 0.0154028104
#> [12,] 0.007728037 0.0056263275 0.007444893 0.006443333 0.0042875141
#> [13,] 0.016216878 0.0082081864 0.013456891 0.011045142 0.0059506446
#> [14,] 0.008907862 0.0044482796 0.005069457 0.018860398 0.0093586451
#> [15,] 0.006324919 0.0193660866 0.007708443 0.013288355 0.0058143532
#> [16,] 0.008371384 0.0037575765 0.005091568 0.003827194 0.0040536500
#> [17,] 0.013731452 0.0068251244 0.010241165 0.003459992 0.0057866108
#> [18,] 0.001164298 0.0026884192 0.005143229 0.004544628 0.0026125429
#> [19,] 0.010421400 0.0080116502 0.006462148 0.006952836 0.0068012611
#> [20,] 0.005295054 0.0064567397 0.009214496 0.003868465 0.0006576598
#> [21,] 0.004889697 0.0050384661 0.005596785 0.005711759 0.0045973119
#> [22,] 0.002600888 0.0034177270 0.004988240 0.015092602 0.0147189221
#> [23,] 0.005754508 0.0083285560 0.005228684 0.004941545 0.0058760088
#> [24,] 0.009864578 0.0099001179 0.002128623 0.003598590 0.0017034994
#> [25,] 0.022534807 0.0100174071 0.011420814 0.005470873 0.0103340313
#> V59 V6 V60 V7 V8 V9
#> [1,] 0.0062000000 0.09580000 0.006700000 0.09900000 0.101800000 0.10300000
#> [2,] 0.0120398757 0.06055533 0.006666193 0.11377531 0.086198057 0.05940030
#> [3,] 0.0181726044 0.09664883 0.010458279 0.02665096 0.089021138 0.00568687
#> [4,] 0.0072079232 0.17793582 0.007234349 0.11924403 0.152232986 0.25377585
#> [5,] 0.0078522950 0.10557255 0.005460343 0.12198900 0.139432420 0.24455554
#> [6,] 0.0203616871 0.12197295 0.007637728 0.09361369 0.128621732 0.10993526
#> [7,] 0.0098477267 0.12576534 0.006533584 0.14130491 0.142143904 0.17187883
#> [8,] 0.0108693900 0.08935765 0.008337111 0.09619966 0.131064284 0.16944526
#> [9,] 0.0068789421 0.03546040 0.003167254 0.08581150 0.220528098 0.39965376
#> [10,] 0.0099963811 0.08075354 0.011884338 0.18950060 0.346437644 0.56638649
#> [11,] 0.0016379854 0.18925224 0.001657810 0.20535670 0.290489513 0.40443412
#> [12,] 0.0025152878 0.10711949 0.002922991 0.13839248 0.145573192 0.28595304
#> [13,] 0.0087586030 0.12571896 0.005131035 0.16121974 0.165741734 0.21431414
#> [14,] 0.0073686313 0.06061490 0.005694096 0.17401778 0.255364240 0.25070682
#> [15,] 0.0164246415 0.20820762 0.006173260 0.20238470 0.181008241 0.13214196
#> [16,] 0.0020950063 0.08948125 0.002709572 0.08049967 0.074359601 0.15515216
#> [17,] 0.0052767868 0.06166672 0.006913108 0.10145834 0.085640652 0.06208243
#> [18,] 0.0026195824 0.11128926 0.004903356 0.11285965 0.102580544 0.13378014
#> [19,] 0.0084808838 0.10650223 0.004124073 0.10020578 0.109613971 0.10959674
#> [20,] 0.0054001232 0.03106340 0.003668074 0.04284453 0.025900821 0.02798079
#> [21,] 0.0139031018 0.07491514 0.008895910 0.06300055 0.110223715 0.16816246
#> [22,] 0.0026437717 0.04465390 0.005169413 0.06253452 -0.003425937 0.05900679
#> [23,] 0.0034165268 0.06055280 0.002603263 0.07971709 0.082275380 0.08073792
#> [24,] 0.0021363735 0.10255771 0.002794456 0.11003349 0.093864510 0.16065221
#> [25,] 0.0009423946 0.01222524 0.004027075 0.02740506 0.058364425 0.11130543
#>
#> $cl
#> [1] M M M M M M M M M M M M M R R R R R R R R R R R R
#> Levels: M R
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.1884058