catboost_predict: Predict based on the model

In nanxstats/stackgbm: Stacked Gradient Boosting Machines

Predict based on the model

Description

Predict based on the model

Usage

catboost_predict(model, pool, prediction_type = "Probability", ...)

Arguments

model	The trained model.
pool	The dataset to predict on.
prediction_type	Prediction type.
...	Additional parameters.

Value

Predicted values.

Examples

sim_data <- msaenet::msaenet.sim.binomial(
  n = 100,
  p = 10,
  rho = 0.6,
  coef = rnorm(5, mean = 0, sd = 10),
  snr = 1,
  p.train = 0.8,
  seed = 42
)

x_train <- catboost_load_pool(data = sim_data$x.tr, label = sim_data$y.tr)
x_test <- catboost_load_pool(data = sim_data$x.te, label = NULL)

fit <- catboost_train(
  x_train,
  NULL,
  params = list(
    loss_function = "Logloss",
    iterations = 100,
    depth = 3,
    logging_level = "Silent"
  )
)

catboost_predict(fit, x_test)

nanxstats/stackgbm documentation built on May 3, 2024, 5:47 p.m.