Create a classifier object for Probabilistic Machine Learning

classifier(
  x,
  y,
  model = c("ranger", "extratrees", "ridge", "bcn", "glmnet", "krr", "xgboost", "svm"),
  pi_method = c("none", "splitconformal", "jackknifeplus", "kdesplitconformal",
    "bootsplitconformal", "kdejackknifeplus", "bootjackknifeplus", "surrsplitconformal",
    "surrjackknifeplus"),
  level = 95,
  B = 100,
  nb_hidden = 0,
  nodes_sim = c("sobol", "halton", "unif"),
  activ = c("relu", "sigmoid", "tanh", "leakyrelu", "elu", "linear"),
  engine = NULL,
  params = NULL,
  seed = 123
)

Arguments

x: Input matrix or data frame of features
y: Vector of target values
model: Model to use for classification
pi_method: Method to use for conformal prediction
level: Confidence level for conformal prediction
B: Number of simulations for conformal prediction
nb_hidden: Number of nodes in the hidden layer
nodes_sim: Type of simulations for hidden nodes
activ: Activation function for hidden layer
engine: Engine to use for fitting the model
params: Additional parameters passed to the model
seed: Reproducibility seed for randomization
...: Additional arguments passed to Classifier$new()

Value

A classifier object of class "classifier"

Examples


X <- iris[, -5]
y <- iris$Species
X_train <- X[1:100, ]
y_train <- y[1:100]
X_test <- X[101:150, ]
y_test <- y[101:150]
clf <- classifier(X_train, y_train, pi_method = "kdesplitconformal", level = 95)
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
predict(clf, newx = X_test)
#> Error: object 'clf' not found
print(mean(y_test == predict(clf, newx = X_test)))
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'print': error in evaluating the argument 'x' in selecting a method for function 'mean': object 'clf' not found