less (version 0.1.0)

KNeighborsClassifier: KNeighborsClassifier

Description

Wrapper R6 Class of caret::knnreg function that can be used for LESSRegressor and LESSClassifier

Value

R6 Class of KNeighborsClassifier

Super classes

less::BaseEstimator -> less::SklearnEstimator -> KNeighborsClassifier

Methods

Public methods

• KNeighborsClassifier$new()

• KNeighborsClassifier$fit()

• KNeighborsClassifier$predict()

• KNeighborsClassifier$get_estimator_type()

• KNeighborsClassifier$clone()

Inherited methods

• less::BaseEstimator$get_all_fields()
• less::BaseEstimator$get_attributes()
• less::BaseEstimator$set_random_state()
• less::SklearnEstimator$get_isFitted()
• less::SklearnEstimator$get_type()

---

Method new()

Creates a new instance of R6 Class of KNeighborsClassifier

Usage

KNeighborsClassifier$new(k = 5)

Arguments

k

Number of neighbors considered (defaults to 5).

Examples

knc <- KNeighborsClassifier$new()
knc <- KNeighborsClassifier$new(k = 5)

---

Method fit()

Fit the k-nearest neighbors regressor from the training set (X, y).

Usage

KNeighborsClassifier$fit(X, y)

Arguments

X

2D matrix or dataframe that includes predictors

y

1D vector or (n,1) dimensional matrix/dataframe that includes labels

Returns

Fitted R6 Class of KNeighborsClassifier

Examples

data(iris)
split_list <- train_test_split(iris, test_size =  0.3)
X_train <- split_list[[1]]
X_test <- split_list[[2]]
y_train <- split_list[[3]]
y_test <- split_list[[4]]
knc <- KNeighborsClassifier$new()
knc$fit(X_train, y_train)

---

Method predict()

Predict regression value for X0.

Usage

KNeighborsClassifier$predict(X0)

Arguments

X0

2D matrix or dataframe that includes predictors

Returns

Factor of the predict classes.

Examples

knc <- KNeighborsClassifier$new()
knc$fit(X_train, y_train)
preds <- knc$predict(X_test)
knc <- KNeighborsClassifier$new()
preds <- knc$fit(X_train, y_train)$predict(X_test)
preds <- KNeighborsClassifier$new()$fit(X_train, y_train)$predict(X_test)
print(caret::confusionMatrix(data=factor(preds), reference = factor(y_test)))

---

Method get_estimator_type()

Auxiliary function returning the estimator type e.g 'regressor', 'classifier'

Usage

KNeighborsClassifier$get_estimator_type()

Examples

knc$get_estimator_type()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

KNeighborsClassifier$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

caret::knn3()

Examples

## ------------------------------------------------
## Method `KNeighborsClassifier$new`
## ------------------------------------------------

knc <- KNeighborsClassifier$new()
knc <- KNeighborsClassifier$new(k = 5)

## ------------------------------------------------
## Method `KNeighborsClassifier$fit`
## ------------------------------------------------

data(iris)
split_list <- train_test_split(iris, test_size =  0.3)
X_train <- split_list[[1]]
X_test <- split_list[[2]]
y_train <- split_list[[3]]
y_test <- split_list[[4]]

knc <- KNeighborsClassifier$new()
knc$fit(X_train, y_train)

## ------------------------------------------------
## Method `KNeighborsClassifier$predict`
## ------------------------------------------------

knc <- KNeighborsClassifier$new()
knc$fit(X_train, y_train)
preds <- knc$predict(X_test)

knc <- KNeighborsClassifier$new()
preds <- knc$fit(X_train, y_train)$predict(X_test)

preds <- KNeighborsClassifier$new()$fit(X_train, y_train)$predict(X_test)
print(caret::confusionMatrix(data=factor(preds), reference = factor(y_test)))

## ------------------------------------------------
## Method `KNeighborsClassifier$get_estimator_type`
## ------------------------------------------------

knc$get_estimator_type()