KNeighborsRegressor {less} | R Documentation |
KNeighborsRegressor
Description
Wrapper R6 Class of caret::knnreg function that can be used for LESSRegressor and LESSClassifier
Value
R6 Class of KNeighborsRegressor
Super classes
less::BaseEstimator
-> less::SklearnEstimator
-> KNeighborsRegressor
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of R6 Class of KNeighborsRegressor
Usage
KNeighborsRegressor$new(k = 5)
Arguments
k
Number of neighbors considered (defaults to 5).
Examples
knr <- KNeighborsRegressor$new() knr <- KNeighborsRegressor$new(k = 5)
Method fit()
Fit the k-nearest neighbors regressor from the training set (X, y).
Usage
KNeighborsRegressor$fit(X, y)
Arguments
X
2D matrix or dataframe that includes predictors
y
1D vector or (n,1) dimensional matrix/dataframe that includes response variables
Returns
Fitted R6 Class of KNeighborsRegressor
Examples
data(abalone) split_list <- train_test_split(abalone[1:100,], test_size = 0.3) X_train <- split_list[[1]] X_test <- split_list[[2]] y_train <- split_list[[3]] y_test <- split_list[[4]] knr <- KNeighborsRegressor$new() knr$fit(X_train, y_train)
Method predict()
Predict regression value for X0.
Usage
KNeighborsRegressor$predict(X0)
Arguments
X0
2D matrix or dataframe that includes predictors
Returns
The predict values.
Examples
knr <- KNeighborsRegressor$new() knr$fit(X_train, y_train) preds <- knr$predict(X_test) knr <- KNeighborsRegressor$new() preds <- knr$fit(X_train, y_train)$predict(X_test) preds <- KNeighborsRegressor$new()$fit(X_train, y_train)$predict(X_test) print(head(matrix(c(y_test, preds), ncol = 2, dimnames = (list(NULL, c("True", "Prediction"))))))
Method get_estimator_type()
Auxiliary function returning the estimator type e.g 'regressor', 'classifier'
Usage
KNeighborsRegressor$get_estimator_type()
Examples
knr$get_estimator_type()
Method clone()
The objects of this class are cloneable with this method.
Usage
KNeighborsRegressor$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
See Also
Examples
## ------------------------------------------------
## Method `KNeighborsRegressor$new`
## ------------------------------------------------
knr <- KNeighborsRegressor$new()
knr <- KNeighborsRegressor$new(k = 5)
## ------------------------------------------------
## Method `KNeighborsRegressor$fit`
## ------------------------------------------------
data(abalone)
split_list <- train_test_split(abalone[1:100,], test_size = 0.3)
X_train <- split_list[[1]]
X_test <- split_list[[2]]
y_train <- split_list[[3]]
y_test <- split_list[[4]]
knr <- KNeighborsRegressor$new()
knr$fit(X_train, y_train)
## ------------------------------------------------
## Method `KNeighborsRegressor$predict`
## ------------------------------------------------
knr <- KNeighborsRegressor$new()
knr$fit(X_train, y_train)
preds <- knr$predict(X_test)
knr <- KNeighborsRegressor$new()
preds <- knr$fit(X_train, y_train)$predict(X_test)
preds <- KNeighborsRegressor$new()$fit(X_train, y_train)$predict(X_test)
print(head(matrix(c(y_test, preds), ncol = 2, dimnames = (list(NULL, c("True", "Prediction"))))))
## ------------------------------------------------
## Method `KNeighborsRegressor$get_estimator_type`
## ------------------------------------------------
knr$get_estimator_type()