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Train a model
Execution format catboost fit -f <file path> [optional parameters] Options Usage examples Train a model with 100 trees on a comma-separated pool with header: catboost fit --learn-set train.csv --test-set...
catboost.save_pool
catboost.save_pool 1. Purpose 2. Arguments catboost.save_pool(data, label = NULL, weight = NULL, baseline = NULL ... The path to the output file that contains the columns description. cd.pool.
Pool
Pool 1. Purpose 2. Parameters 3. Attributes 4. Methods 5. Usage examples class Pool(data, label=None, cat_features=None, text_features=None, column_description=None, pairs=None...
catboost.load_pool
catboost.load_pool 1. Purpose 2. Arguments 3. Examples catboost.load_pool(data, label = NULL, cat_features = NULL, column_description = NULL, pairs = NULL...
Pool initialization
Pool Pool initialization. A list of possible methods to load the dataset from a file is given in the table below. Method Usage example Information Use the default columns description Pool...
R package training parameters
True if a validation set is input (the train_pool parameter is defined) and at least one of the label values of objects in this set differs from the others. False otherwise.
Python package training parameters
Specifies options: Langevin: true, DiffusionTemperature: objects in learn pool count, ModelShrinkRate: 1 / (2. * objects in learn pool count) False. CPU only allow_const_label. bool.
FeaturesData
CatBoostClassifier with Pool and FeaturesData. ... Otherwise, pass the input dataset and target variables directly to the Pool class.
fit
Load the dataset using Pool, train it with CatBoostClassifier and make a prediction. fit 1. Method call format 2. Parameters 3. Usage examples Train a model.
Usage examples
CatBoostClassifier class with array-like data. Load the dataset using Pool, train it with CatBoostClassifier and make a prediction.
Quick start
test_pool = Pool(test_data) # specify training parameters via map param = {\'iterations\':5} model = CatBoost(param) #train the model model.fit(train_pool)...
Usage examples
catboost.load_pool(dataset, label = label_values) model <- catboost.train(pool, params = fit_params) Load a dataset with numerical and categorical features...
Quick start
Quick start. 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...
is_quantized
is_quantized 1. Method call format 2. Usage examples Check whether the pool is quantized. ... train_dataset = Pool(train_data, train_labels) print(train_dataset.is_quantized()) train_dataset.quantize...
plot_tree
Required parameter pool. catboost.Pool. An optional parameter for models that contain only float features. Allows to pass a pool and label features with their external indices from this pool.
plot_tree
Required parameter pool. catboost.Pool. An optional parameter for models that contain only float features. Allows to pass a pool and label features with their external indices from this pool.
plot_tree
Required parameter pool. catboost.Pool. An optional parameter for models that contain only float features. Allows to pass a pool and label features with their external indices from this pool.
save
save 1. Method call format 2. Parameters 3. Example Save the quantized pool to a file. ... Default value fname. string. The name of the output file to save the pool to.
set_baseline
Pool set_baseline. Set initial formula values for all input objects. ... Required parameter Example import numpy as np from catboost import Pool train_data = [[76, \'blvd\', 41, 50, 7], [75, \'today\', 57, 0, 48], [70...
plot_predictions
train_pool_slice, features_to_change=prediction_diff[\"Feature Id\"][:2], plot=True, plot_file=\"plot_predictions_file.html\") An example of the first plotted chart
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