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: 0.0286 0.3039 0.2988 0.4250 0.6343 0.8198 1.0000 0.9988 0.9508 0.9025
#>   2: 0.0317 0.3513 0.1786 0.0658 0.0513 0.3752 0.5419 0.5440 0.5150 0.4262
#>   3: 0.0223 0.1487 0.1156 0.1654 0.3833 0.3598 0.1713 0.1136 0.0349 0.3796
#>   4: 0.0164 0.0251 0.0801 0.1056 0.1266 0.0890 0.0198 0.1133 0.2826 0.3234
#>   5: 0.0039 0.0452 0.0492 0.0996 0.1424 0.1194 0.0628 0.0907 0.1177 0.1429
#>  ---                                                                      
#> 135: 0.0187 0.2684 0.3108 0.2933 0.2275 0.0994 0.1801 0.2200 0.2732 0.2862
#> 136: 0.0323 0.2154 0.3085 0.3425 0.2990 0.1402 0.1235 0.1534 0.1901 0.2429
#> 137: 0.0522 0.2529 0.2716 0.2374 0.1878 0.0983 0.0683 0.1503 0.1723 0.2339
#> 138: 0.0303 0.2354 0.2898 0.2812 0.1578 0.0273 0.0673 0.1444 0.2070 0.2645
#> 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: 0.7234 0.0453 0.5122 0.2074 0.3985 0.5890 0.2872 0.2043 0.5782 0.5389
#>   2: 0.2024 0.0956 0.4233 0.7723 0.9735 0.9390 0.5559 0.5268 0.6826 0.5713
#>   3: 0.7401 0.0375 0.9925 0.9802 0.8890 0.6712 0.4286 0.3374 0.7366 0.9611
#>   4: 0.3238 0.0173 0.4333 0.6068 0.7652 0.9203 0.9719 0.9207 0.7545 0.8289
#>   5: 0.1223 0.0063 0.1104 0.1847 0.3715 0.4382 0.5707 0.6654 0.7476 0.7654
#>  ---                                                                      
#> 135: 0.2034 0.0346 0.1740 0.4130 0.6879 0.8120 0.8453 0.8919 0.9300 0.9987
#> 136: 0.2120 0.0101 0.2395 0.3272 0.5949 0.8302 0.9045 0.9888 0.9912 0.9448
#> 137: 0.1962 0.0437 0.1395 0.3164 0.5888 0.7631 0.8473 0.9424 0.9986 0.9699
#> 138: 0.2828 0.0353 0.4293 0.5685 0.6990 0.7246 0.7622 0.9242 1.0000 0.9979
#> 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: 0.3750 0.3411 0.0277 0.5067 0.5580 0.4778 0.3299 0.2198 0.1407 0.2856
#>   2: 0.5429 0.2177 0.1321 0.2149 0.5811 0.6323 0.2965 0.1873 0.2969 0.5163
#>   3: 0.7353 0.4856 0.0484 0.1594 0.3007 0.4096 0.3170 0.3305 0.3408 0.2186
#>   4: 0.8907 0.7309 0.0347 0.6896 0.5829 0.4935 0.3101 0.0306 0.0244 0.1108
#>   5: 0.8555 0.9720 0.0152 0.9221 0.7502 0.7209 0.7757 0.6055 0.5021 0.4499
#>  ---                                                                      
#> 135: 1.0000 0.8104 0.0168 0.6199 0.6041 0.5547 0.4160 0.1472 0.0849 0.0608
#> 136: 1.0000 0.9092 0.0298 0.7412 0.7691 0.7117 0.5304 0.2131 0.0928 0.1297
#> 137: 1.0000 0.8630 0.0180 0.6979 0.7717 0.7305 0.5197 0.1786 0.1098 0.1446
#> 138: 0.8297 0.7032 0.0490 0.7141 0.6893 0.4961 0.2584 0.0969 0.0776 0.0364
#> 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: 0.3807 0.4158 0.4054 0.0174 0.3296 0.2707 0.2650 0.0723 0.1238 0.1192
#>   2: 0.6153 0.4283 0.5479 0.1408 0.6133 0.5017 0.2377 0.1957 0.1749 0.1304
#>   3: 0.2463 0.2726 0.1680 0.0475 0.2792 0.2558 0.1740 0.2121 0.1099 0.0985
#>   4: 0.1594 0.1371 0.0696 0.0070 0.0452 0.0620 0.1421 0.1597 0.1384 0.0372
#>   5: 0.3947 0.4281 0.4427 0.0336 0.3749 0.1972 0.0511 0.0793 0.1269 0.1533
#>  ---                                                                      
#> 135: 0.0969 0.1411 0.1676 0.0177 0.1200 0.1201 0.1036 0.1977 0.1339 0.0902
#> 136: 0.1159 0.1226 0.1768 0.0564 0.0345 0.1562 0.0824 0.1149 0.1694 0.0954
#> 137: 0.1066 0.1440 0.1929 0.0292 0.0325 0.1490 0.0328 0.0537 0.1309 0.0910
#> 138: 0.1572 0.1823 0.1349 0.0608 0.0849 0.0492 0.1367 0.1552 0.1548 0.1319
#> 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: 0.1089 0.0623 0.0494 0.0264 0.0384 0.0081 0.0104 0.0045 0.0014 0.0038
#>   2: 0.0597 0.1124 0.1047 0.0507 0.1674 0.0159 0.0195 0.0201 0.0248 0.0131
#>   3: 0.1271 0.1459 0.1164 0.0777 0.0647 0.0439 0.0061 0.0145 0.0128 0.0145
#>   4: 0.0688 0.0867 0.0513 0.0092 0.0187 0.0198 0.0118 0.0090 0.0223 0.0179
#>   5: 0.0690 0.0402 0.0534 0.0228 0.0310 0.0073 0.0062 0.0062 0.0120 0.0052
#>  ---                                                                      
#> 135: 0.1085 0.1521 0.1363 0.0858 0.0393 0.0290 0.0203 0.0116 0.0098 0.0199
#> 136: 0.0080 0.0790 0.1255 0.0647 0.0760 0.0179 0.0051 0.0061 0.0093 0.0135
#> 137: 0.0757 0.1059 0.1005 0.0535 0.0351 0.0235 0.0155 0.0160 0.0029 0.0051
#> 138: 0.0985 0.1258 0.0954 0.0489 0.0167 0.0241 0.0042 0.0086 0.0046 0.0126
#> 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: 0.0013 0.0089 0.0057 0.0027 0.0051 0.0990 0.0062 0.1201 0.1833 0.2105
#>   2: 0.0070 0.0138 0.0092 0.0143 0.0036 0.1710 0.0103 0.0731 0.1401 0.2083
#>   3: 0.0058 0.0049 0.0065 0.0093 0.0059 0.0591 0.0022 0.0753 0.0098 0.0684
#>   4: 0.0084 0.0068 0.0032 0.0035 0.0056 0.0671 0.0040 0.1056 0.0697 0.0962
#>   5: 0.0056 0.0093 0.0042 0.0003 0.0053 0.0284 0.0036 0.0396 0.0272 0.0323
#>  ---                                                                      
#> 135: 0.0033 0.0101 0.0065 0.0115 0.0193 0.1630 0.0157 0.2028 0.1694 0.2328
#> 136: 0.0063 0.0063 0.0034 0.0032 0.0062 0.0958 0.0067 0.0990 0.1018 0.1030
#> 137: 0.0062 0.0089 0.0140 0.0138 0.0077 0.1171 0.0031 0.1257 0.1178 0.1258
#> 138: 0.0036 0.0035 0.0034 0.0079 0.0036 0.1354 0.0048 0.1465 0.1123 0.1945
#> 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 R R R R 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 
#>  0.1884058