Fast Nearest Neighbour Classification

Fast Nearest Neighbour Classification. Calls FNN::knn() from FNN.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.fnn")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3extralearners, FNN

Parameters

Id	Type	Default	Levels	Range
k	integer	1		\([1, \infty)\)
algorithm	character	kd_tree	kd_tree, cover_tree, brute	-

References

Boltz, Sylvain, Debreuve, Eric, Barlaud, Michel (2007). “kNN-based high-dimensional Kullback-Leibler distance for tracking.” In Eighth International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS'07), 16–16. IEEE.

See also

• Dictionary of Learners: mlr3::mlr_learners.

• 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.

Author

be-marc

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifFNN

Methods

Public methods

• LearnerClassifFNN$new()

• LearnerClassifFNN$clone()

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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifFNN$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifFNN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.fnn")
print(learner)
#> 
#> ── <LearnerClassifFNN> (classif.fnn): Fast Nearest Neighbour ───────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3extralearners, and FNN
#> • Predict Types: [response] and prob
#> • Feature Types: integer and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: multiclass and twoclass
#> • Other settings: use_weights = 'error'

# 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)
#> $train
#>          V1    V10    V11    V12    V13    V14    V15    V16    V17    V18
#>       <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>
#>   1: 0.0200 0.2111 0.1609 0.1582 0.2238 0.0645 0.0660 0.2273 0.3100 0.2999
#>   2: 0.0262 0.6194 0.6333 0.7060 0.5544 0.5320 0.6479 0.6931 0.6759 0.7551
#>   3: 0.0762 0.4459 0.4152 0.3952 0.4256 0.4135 0.4528 0.5326 0.7306 0.6193
#>   4: 0.0317 0.3513 0.1786 0.0658 0.0513 0.3752 0.5419 0.5440 0.5150 0.4262
#>   5: 0.0519 0.2838 0.2802 0.3086 0.2657 0.3801 0.5626 0.4376 0.2617 0.1199
#>  ---                                                                      
#> 135: 0.0131 0.3193 0.3468 0.3738 0.3055 0.1926 0.1385 0.2122 0.2758 0.4576
#> 136: 0.0335 0.2660 0.3188 0.3553 0.3116 0.1965 0.1780 0.2794 0.2870 0.3969
#> 137: 0.0272 0.3997 0.3941 0.3309 0.2926 0.1760 0.1739 0.2043 0.2088 0.2678
#> 138: 0.0187 0.2684 0.3108 0.2933 0.2275 0.0994 0.1801 0.2200 0.2732 0.2862
#> 139: 0.0260 0.2354 0.2720 0.2442 0.1665 0.0336 0.1302 0.1708 0.2177 0.3175
#>         V19     V2    V20    V21    V22    V23    V24    V25    V26    V27
#>       <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>
#>   1: 0.5078 0.0371 0.4797 0.5783 0.5071 0.4328 0.5550 0.6711 0.6415 0.7104
#>   2: 0.8929 0.0582 0.8619 0.7974 0.6737 0.4293 0.3648 0.5331 0.2413 0.5070
#>   3: 0.2032 0.0666 0.4636 0.4148 0.4292 0.5730 0.5399 0.3161 0.2285 0.6995
#>   4: 0.2024 0.0956 0.4233 0.7723 0.9735 0.9390 0.5559 0.5268 0.6826 0.5713
#>   5: 0.6676 0.0548 0.9402 0.7832 0.5352 0.6809 0.9174 0.7613 0.8220 0.8872
#>  ---                                                                      
#> 135: 0.6487 0.0387 0.7154 0.8010 0.7924 0.8793 1.0000 0.9865 0.9474 0.9474
#> 136: 0.5599 0.0258 0.6936 0.7969 0.7452 0.8203 0.9261 0.8810 0.8814 0.9301
#> 137: 0.2434 0.0378 0.1839 0.2802 0.6172 0.8015 0.8313 0.8440 0.8494 0.9168
#> 138: 0.2034 0.0346 0.1740 0.4130 0.6879 0.8120 0.8453 0.8919 0.9300 0.9987
#> 139: 0.3714 0.0363 0.4552 0.5700 0.7397 0.8062 0.8837 0.9432 1.0000 0.9375
#>         V28    V29     V3    V30    V31    V32    V33    V34    V35    V36
#>       <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>
#>   1: 0.8080 0.6791 0.0428 0.3857 0.1307 0.2604 0.5121 0.7547 0.8537 0.8507
#>   2: 0.8533 0.6036 0.1099 0.8514 0.8512 0.5045 0.1862 0.2709 0.4232 0.3043
#>   3: 1.0000 0.7262 0.0481 0.4724 0.5103 0.5459 0.2881 0.0981 0.1951 0.4181
#>   4: 0.5429 0.2177 0.1321 0.2149 0.5811 0.6323 0.2965 0.1873 0.2969 0.5163
#>   5: 0.6091 0.2967 0.0842 0.1103 0.1318 0.0624 0.0990 0.4006 0.3666 0.1050
#>  ---                                                                      
#> 135: 0.9315 0.8326 0.0329 0.6213 0.3772 0.2822 0.2042 0.2190 0.2223 0.1327
#> 136: 0.9955 0.8576 0.0398 0.6069 0.3934 0.2464 0.1645 0.1140 0.0956 0.0080
#> 137: 1.0000 0.7896 0.0488 0.5371 0.6472 0.6505 0.4959 0.2175 0.0990 0.0434
#> 138: 1.0000 0.8104 0.0168 0.6199 0.6041 0.5547 0.4160 0.1472 0.0849 0.0608
#> 139: 0.7603 0.7123 0.0136 0.8358 0.7622 0.4567 0.1715 0.1549 0.1641 0.1869
#>         V37    V38    V39     V4    V40    V41    V42    V43    V44    V45
#>       <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>
#>   1: 0.6692 0.6097 0.4943 0.0207 0.2744 0.0510 0.2834 0.2825 0.4256 0.2641
#>   2: 0.6116 0.6756 0.5375 0.1083 0.4719 0.4647 0.2587 0.2129 0.2222 0.2111
#>   3: 0.4604 0.3217 0.2828 0.0394 0.2430 0.1979 0.2444 0.1847 0.0841 0.0692
#>   4: 0.6153 0.4283 0.5479 0.1408 0.6133 0.5017 0.2377 0.1957 0.1749 0.1304
#>   5: 0.1915 0.3930 0.4288 0.0319 0.2546 0.1151 0.2196 0.1879 0.1437 0.2146
#>  ---                                                                      
#> 135: 0.0521 0.0618 0.1416 0.0078 0.1460 0.0846 0.1055 0.1639 0.1916 0.2085
#> 136: 0.0702 0.0936 0.0894 0.0570 0.1127 0.0873 0.1020 0.1964 0.2256 0.1814
#> 137: 0.1708 0.1979 0.1880 0.0848 0.1108 0.1702 0.0585 0.0638 0.1391 0.0638
#> 138: 0.0969 0.1411 0.1676 0.0177 0.1200 0.1201 0.1036 0.1977 0.1339 0.0902
#> 139: 0.2655 0.1713 0.0959 0.0272 0.0768 0.0847 0.2076 0.2505 0.1862 0.1439
#>         V46    V47    V48    V49     V5    V50    V51    V52    V53    V54
#>       <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>
#>   1: 0.1386 0.1051 0.1343 0.0383 0.0954 0.0324 0.0232 0.0027 0.0065 0.0159
#>   2: 0.0176 0.1348 0.0744 0.0130 0.0974 0.0106 0.0033 0.0232 0.0166 0.0095
#>   3: 0.0528 0.0357 0.0085 0.0230 0.0590 0.0046 0.0156 0.0031 0.0054 0.0105
#>   4: 0.0597 0.1124 0.1047 0.0507 0.1674 0.0159 0.0195 0.0201 0.0248 0.0131
#>   5: 0.2360 0.1125 0.0254 0.0285 0.1158 0.0178 0.0052 0.0081 0.0120 0.0045
#>  ---                                                                      
#> 135: 0.2335 0.1964 0.1300 0.0633 0.0721 0.0183 0.0137 0.0150 0.0076 0.0032
#> 136: 0.2012 0.1688 0.1037 0.0501 0.0529 0.0136 0.0130 0.0120 0.0039 0.0053
#> 137: 0.0581 0.0641 0.1044 0.0732 0.1127 0.0275 0.0146 0.0091 0.0045 0.0043
#> 138: 0.1085 0.1521 0.1363 0.0858 0.0393 0.0290 0.0203 0.0116 0.0098 0.0199
#> 139: 0.1470 0.0991 0.0041 0.0154 0.0214 0.0116 0.0181 0.0146 0.0129 0.0047
#>         V55    V56    V57    V58    V59     V6    V60     V7     V8     V9
#>       <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>  <num>
#>   1: 0.0072 0.0167 0.0180 0.0084 0.0090 0.0986 0.0032 0.1539 0.1601 0.3109
#>   2: 0.0180 0.0244 0.0316 0.0164 0.0095 0.2280 0.0078 0.2431 0.3771 0.5598
#>   3: 0.0110 0.0015 0.0072 0.0048 0.0107 0.0649 0.0094 0.1209 0.2467 0.3564
#>   4: 0.0070 0.0138 0.0092 0.0143 0.0036 0.1710 0.0103 0.0731 0.1401 0.2083
#>   5: 0.0121 0.0097 0.0085 0.0047 0.0048 0.0922 0.0053 0.1027 0.0613 0.1465
#>  ---                                                                      
#> 135: 0.0037 0.0071 0.0040 0.0009 0.0015 0.1341 0.0085 0.1626 0.1902 0.2610
#> 136: 0.0062 0.0046 0.0045 0.0022 0.0005 0.1091 0.0031 0.1709 0.1684 0.1865
#> 137: 0.0043 0.0098 0.0054 0.0051 0.0065 0.1103 0.0103 0.1349 0.2337 0.3113
#> 138: 0.0033 0.0101 0.0065 0.0115 0.0193 0.1630 0.0157 0.2028 0.1694 0.2328
#> 139: 0.0039 0.0061 0.0040 0.0036 0.0061 0.0338 0.0115 0.0655 0.1400 0.1843
#> 
#> $cl
#>   [1] R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R
#>  [38] R R R R R R R R R R R R R R R R R R R R R R R R R R R M M M M M M M M M M
#>  [75] M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M
#> [112] M M M M M M M M M M M M M M M M M M M M M M M M M M M M
#> Levels: M R
#> 


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2753623