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Wrapper R6 Class of e1071::svm function that can be used for LESSRegressor and LESSClassifier
R6 Class of SVC
less::BaseEstimator
-> less::SklearnEstimator
-> SVC
new()
Creates a new instance of R6 Class of SVC
SVC$new(
scale = TRUE,
kernel = "radial",
degree = 3,
gamma = NULL,
coef0 = 0,
cost = 1,
cache_size = 40,
tolerance = 0.001,
epsilon = 0.1,
shrinking = TRUE,
cross = 0,
probability = FALSE,
fitted = TRUE
)
scale
A logical vector indicating the variables to be scaled. If scale is of length 1, the value is recycled as many times as needed. Per default, data are scaled internally (both x and y variables) to zero mean and unit variance. The center and scale values are returned and used for later predictions (default: TRUE)
kernel
The kernel used in training and predicting. Possible values are: "linear", "polynomial", "radial", "sigmoid" (default is "radial")
degree
Parameter needed for kernel of type polynomial (default: 3)
gamma
Parameter needed for all kernels except linear (default: 1/(data dimension))
coef0
Parameter needed for kernels of type polynomial and sigmoid (default: 0)
cost
Cost of constraints violation (default: 1)—it is the ‘C’-constant of the regularization term in the Lagrange formulation (default: 1)
cache_size
Cache memory in MB (default: 40)
tolerance
Tolerance of termination criterion (default: 0.001)
epsilon
Epsilon in the insensitive-loss function (default: 0.1)
shrinking
Option whether to use the shrinking-heuristics (default: TRUE)
cross
If a integer value k>0 is specified, a k-fold cross validation on the training data is performed to assess the quality of the model: the accuracy rate for classification and the Mean Squared Error for regression (default: 0)
probability
Logical indicating whether the model should allow for probability predictions (default: FALSE)
fitted
Logical indicating whether the fitted values should be computed and included in the model or not (default: TRUE)
svc <- SVC$new()
svc <- SVC$new(kernel = "polynomial")
fit()
Fit the SVM model from the training set (X, y).
SVC$fit(X, y)
X
2D matrix or dataframe that includes predictors
y
1D vector or (n,1) dimensional matrix/dataframe that includes labels
Fitted R6 Class of SVC
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]]svc <- SVC$new()
svc$fit(X_train, y_train)
X0
2D matrix or dataframe that includes predictors
Factor of the predict classes.
svc <- SVC$new()
svc$fit(X_train, y_train)
preds <- svc$predict(X_test)svc <- SVC$new()
preds <- svc$fit(X_train, y_train)$predict(X_test)
preds <- SVC$new()$fit(X_train, y_train)$predict(X_test)
print(caret::confusionMatrix(data=preds, reference = factor(y_test)))
get_estimator_type()
Auxiliary function returning the estimator type e.g 'regressor', 'classifier'
SVC$get_estimator_type()
svc$get_estimator_type()
clone()
The objects of this class are cloneable with this method.
SVC$clone(deep = FALSE)
deep
Whether to make a deep clone.
## ------------------------------------------------
## Method `SVC$new`
## ------------------------------------------------
svc <- SVC$new()
svc <- SVC$new(kernel = "polynomial")
## ------------------------------------------------
## Method `SVC$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]]
svc <- SVC$new()
svc$fit(X_train, y_train)
## ------------------------------------------------
## Method `SVC$predict`
## ------------------------------------------------
svc <- SVC$new()
svc$fit(X_train, y_train)
preds <- svc$predict(X_test)
svc <- SVC$new()
preds <- svc$fit(X_train, y_train)$predict(X_test)
preds <- SVC$new()$fit(X_train, y_train)$predict(X_test)
print(caret::confusionMatrix(data=preds, reference = factor(y_test)))
## ------------------------------------------------
## Method `SVC$get_estimator_type`
## ------------------------------------------------
svc$get_estimator_type()
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