superml (version 0.5.7)

KNNTrainer: K Nearest Neighbours Trainer

Description

Trains a k nearest neighbour model using fast search algorithms. KNN is a supervised learning algorithm which is used for both regression and classification problems.

Format

R6Class object.

Usage

For usage details see Methods, Arguments and Examples sections.

bst = KNNTrainer$new(k=1, prob=FALSE, algorithm=NULL, type="class")
bst$fit(X_train, X_test, "target")
bst$predict(type)

Methods

$new()

Initialise the instance of the trainer

$fit()

trains the knn model and stores the test prediction

$predict()

returns predictions

Arguments

k

number of neighbours to predict

prob

if probability should be computed, default=FALSE

algorithm

algorithm used to train the model, possible values are 'kd_tree','cover_tree','brute'

type

type of problem to solve i.e. regression or classification, possible values are 'reg' or 'class'

Public fields

k

number of neighbours to predict

prob

if probability should be computed, default=FALSE

algorithm

algorithm used to train the model, possible values are 'kd_tree','cover_tree','brute'

type

type of problem to solve i.e. regression or classification, possible values are 'reg' or 'class'

model

for internal use

Methods

Public methods

• KNNTrainer$new()

• KNNTrainer$fit()

• KNNTrainer$predict()

• KNNTrainer$clone()

---

Method new()

Usage

KNNTrainer$new(k, prob, algorithm, type)

Arguments

k

k number of neighbours to predict

prob

if probability should be computed, default=FALSE

algorithm

algorithm used to train the model, possible values are 'kd_tree','cover_tree','brute'

type

type of problem to solve i.e. regression or classification, possible values are 'reg' or 'class'

Details

Create a new `KNNTrainer` object.

Returns

A `KNNTrainer` object.

Examples

data("iris")

iris$Species <- as.integer(as.factor(iris$Species))

xtrain <- iris[1:100,] xtest <- iris[101:150,]

bst <- KNNTrainer$new(k=3, prob=TRUE, type="class") bst$fit(xtrain, xtest, 'Species') pred <- bst$predict(type="raw")

---

Method fit()

Usage

KNNTrainer$fit(train, test, y)

Arguments

train

data.frame or matrix

test

data.frame or matrix

y

character, name of target variable

Details

Trains the KNNTrainer model

Returns

NULL

Examples

data("iris")

iris$Species <- as.integer(as.factor(iris$Species))

xtrain <- iris[1:100,] xtest <- iris[101:150,]

bst <- KNNTrainer$new(k=3, prob=TRUE, type="class") bst$fit(xtrain, xtest, 'Species')

---

Method predict()

Usage

KNNTrainer$predict(type = "raw")

Arguments

type

character, 'raw' for labels else 'prob'

Details

Predits the nearest neigbours for test data

Returns

a list of predicted neighbours

Examples

data("iris")

iris$Species <- as.integer(as.factor(iris$Species))

xtrain <- iris[1:100,] xtest <- iris[101:150,]

bst <- KNNTrainer$new(k=3, prob=TRUE, type="class") bst$fit(xtrain, xtest, 'Species') pred <- bst$predict(type="raw")

---

Method clone()

The objects of this class are cloneable with this method.

Usage

KNNTrainer$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

data("iris")

iris$Species <- as.integer(as.factor(iris$Species))

xtrain <- iris[1:100,]
xtest <- iris[101:150,]

bst <- KNNTrainer$new(k=3, prob=TRUE, type="class")
bst$fit(xtrain, xtest, 'Species')
pred <- bst$predict(type="raw")

## ------------------------------------------------
## Method `KNNTrainer$new`
## ------------------------------------------------

data("iris")

iris$Species <- as.integer(as.factor(iris$Species))

xtrain <- iris[1:100,]
xtest <- iris[101:150,]

bst <- KNNTrainer$new(k=3, prob=TRUE, type="class")
bst$fit(xtrain, xtest, 'Species')
pred <- bst$predict(type="raw")

## ------------------------------------------------
## Method `KNNTrainer$fit`
## ------------------------------------------------

data("iris")

iris$Species <- as.integer(as.factor(iris$Species))

xtrain <- iris[1:100,]
xtest <- iris[101:150,]

bst <- KNNTrainer$new(k=3, prob=TRUE, type="class")
bst$fit(xtrain, xtest, 'Species')

## ------------------------------------------------
## Method `KNNTrainer$predict`
## ------------------------------------------------

data("iris")

iris$Species <- as.integer(as.factor(iris$Species))

xtrain <- iris[1:100,]
xtest <- iris[101:150,]

bst <- KNNTrainer$new(k=3, prob=TRUE, type="class")
bst$fit(xtrain, xtest, 'Species')
pred <- bst$predict(type="raw")