| Title: | Classification, Regression, Clustering with K Nearest Neighbors |
|---|---|
| Description: | Classification, regression, and clustering with k nearest neighbors algorithm. Implements several distance and similarity measures, covering continuous and logical features. Outputs ranked neighbors. Most features of this package are directly based on the PMML specification for KNN. |
| Authors: | Dmitriy Bolotov [aut, cre], Software AG [cph] |
| Maintainer: | Dmitriy Bolotov <[email protected]> |
| License: | GPL (>= 2.1) |
| Version: | 1.0.3 |
| Built: | 2024-11-01 11:15:14 UTC |
| Source: | https://github.com/cran/neighbr |
Calculate the distance between two vectors.
distance(x, y, measure)distance(x, y, measure)
x, y
|
Numeric vectors. |
measure |
Distance measure ("euclidean" or "squared_euclidean"). |
Distance measures in this package are based on those defined
in the PMML specification.
Distances are calculated using the following equations:
Euclidean:
Squared euclidean:
The input vectors must be of the same length.
The distance between x and y.
similarity,
PMML comparison measures
distance(c(-0.5,1),c(0.4,1.6),"euclidean") distance(c(-0.5,1),c(0.4,1.6),"squared_euclidean")distance(c(-0.5,1),c(0.4,1.6),"euclidean") distance(c(-0.5,1),c(0.4,1.6),"squared_euclidean")
Classification, regression, and clustering with k nearest neighbors.
knn( train_set, test_set, k = 3, categorical_target = NULL, continuous_target = NULL, comparison_measure, categorical_scoring_method = "majority_vote", continuous_scoring_method = "average", return_ranked_neighbors = 0, id = NULL )knn( train_set, test_set, k = 3, categorical_target = NULL, continuous_target = NULL, comparison_measure, categorical_scoring_method = "majority_vote", continuous_scoring_method = "average", return_ranked_neighbors = 0, id = NULL )
train_set |
Data frame containing the training instances, with features and any targets and IDs. |
test_set |
Data frame containing the test instances, with feature columns only. |
k |
Number of nearest neighbors. |
categorical_target |
Categorical target variable. |
continuous_target |
Continuous target variable. |
comparison_measure |
Distance or similarity measure. |
categorical_scoring_method |
Categorical scoring method. |
continuous_scoring_method |
Continuous scoring method. |
return_ranked_neighbors |
Number of ranked neighbors to return. A 0 indicates no ranked neighbors. Must not exceed k. |
id |
Column containing unique identifiers for each row in the training
set. Only used when |
The algorithm can score data with continuous or logical features.
The algorithm can predict either a continuous or categorical target, or both (but no more than one of each), as well as return the closest neighbors ranked by distance or similarity. If no continuous or categorical target is provided, return_ranked_neighbors must be non-zero, and ranked neighbors will be returned.
There is no predict method for knn. The scored test set is returned
as part of the neighbr object. The data to be scored must be passed in
with the training data to knn().
Supported distance measures (used with continuous features): euclidean, squared_euclidean.
Supported similarity measures (used with logical features): simple_matching, jaccard, tanimoto.
Currently, only one type of categorical_scoring_method and continuous_scoring_method are supported (majority vote and average, respectively).
Logical features must consist of 0,1 or TRUE,FALSE values.
Categorical non-logical features must be transformed before being used.
The categorical target does not have to be of factor class, but is assumed to be not continuous.
The distance and similarity measures in this package are based on those defined in the PMML specification.
Several of the elements in the returned list are only used when converting
the knn model to PMML (for example, function_name).
For more details and examples, see the vignette by running the following:
vignette("neighbr-help")
An object of class neighbr, which is a list of the following:
call |
The original call to |
k |
Number of nearest neighbors. |
categorical_target |
Categorical target variable. |
continuous_target |
Continuous target variable. |
comparison_measure |
Distance or similarity measure. |
categorical_scoring_method |
Categorical scoring method. |
continuous_scoring_method |
Continuous scoring method. |
return_ranked_neighbors |
Number of ranked neighbors to return. |
id |
ID variable. |
features |
List of feature names. |
function_name |
Function name, used when generating PMML. One of "classification", "regression", "clustering", or "mixed". |
categorical_levels |
Levels of the categorical target. |
num_train_rows |
Number of training instances. |
num_test_rows |
Number of test instances. |
train_set |
Data frame with training instances. |
test_set_scores |
Data frame with scores for the test set. |
similarity, distance,
PMML KNN specification
# continuous features with continuous target, categorical target, # and neighbor ranking data(iris) # add an ID column to the data for neighbor ranking iris$ID <- c(1:150) # train set contains all predicted variables, features, and ID column train_set <- iris[1:145,] # omit predicted variables or ID column from test set test_set <- iris[146:150,-c(4,5,6)] fit <- knn(train_set=train_set,test_set=test_set, k=5, categorical_target="Species", continuous_target= "Petal.Width", comparison_measure="euclidean", return_ranked_neighbors=3, id="ID")# continuous features with continuous target, categorical target, # and neighbor ranking data(iris) # add an ID column to the data for neighbor ranking iris$ID <- c(1:150) # train set contains all predicted variables, features, and ID column train_set <- iris[1:145,] # omit predicted variables or ID column from test set test_set <- iris[146:150,-c(4,5,6)] fit <- knn(train_set=train_set,test_set=test_set, k=5, categorical_target="Species", continuous_target= "Petal.Width", comparison_measure="euclidean", return_ranked_neighbors=3, id="ID")
Classification, regression, and clustering with k nearest neighbors.
The package contains a function for running the nearest neighbors algorithm, as well as functions to directly compute distances between two vectors.
knn documents the main knn function.
distance and similarity provide details on standalone measures.
For more details and examples, see the vignette by running the following:
vignette("neighbr-help")
Calculate the similarity between two vectors of logicals.
similarity(x, y, measure)similarity(x, y, measure)
x, y
|
Logical or numeric vectors. |
measure |
Similarity measure ("simple_matching", "jaccard", or "tanimoto") |
Input vectors must consist of logical or numeric elements TRUE,FALSE or 0,1 (not factors). Similarity measures in this package are based on those defined in the PMML specification. Similarity ranges from 0 (no similarity) to 1 (identical).
For logical vectors x and y, we define the following:
a11 = number of times where x_i=1 and y_i=1a10 = number of times where x_i=1 and y_i=0a01 = number of times where x_i=0 and y_i=1a00 = number of times where x_i=0 and y_i=0
Similarities are calculated using the following formulas:
Simple matching:
Jaccard:
Tanimoto:
The similarity between x and y.
distance,
PMML comparison measures
similarity(c(0,1,1),c(0,0,1),"simple_matching") similarity(c(0,1,1),c(0,0,1),"jaccard") similarity(as.logical(c(0,1,1)),as.logical(c(0,0,1)),"tanimoto")similarity(c(0,1,1),c(0,0,1),"simple_matching") similarity(c(0,1,1),c(0,0,1),"jaccard") similarity(as.logical(c(0,1,1)),as.logical(c(0,0,1)),"tanimoto")