To get started: Prepare a dataset using the catboost.load_pool function: library(catboost) features <- data.frame(feature1 = c(1, 2, 3), feature2 = c('A', 'B', 'C')) labels <- c(0, 0, 1) train_pool <- catboost.load_pool(data = features, label = labels) The dataset is created from a synthetic data.frame called features in this example. The data argument can also reference a dataset file or a matrix of numerical features. Train the model using the catboost.train function:model <- catboost.train(train_pool, NULL, params = list(loss_function = 'Logloss', iterations = 100, metric_period=10)) The second argument in this example (test_pool) is set to NULL. It can also be used to pass a test dataset (the labelled data used for estimating the prediction error while training). The params argument is used to specify the training parameters. Apply the trained model using the catboost.predict function: real_data <- data.frame(feature1 = c(2, 1, 3), feature2 = c('D', 'B', 'C')) real_pool <- catboost.load_pool(real_data) prediction…

Эту функцию называют также «логлосс» (logloss / log_loss), перекрёстной / кросс-энтропией (Cross Entropy) и часто используют в задачах классификации. Разберёмся, почему её используют и какой смысл …