from catboost import Pool # Create a pool object from a CSV file train_data = Pool('train.csv') # Create a pool object with additional parameters valid_data = Pool('valid.csv', delimiter='\t', has_header=True)
import pandas as pd from catboost import Pool # Create a Pandas data frame df = pd.read_csv('train.csv') # Create a pool object from the data frame train_data = Pool(data=df, label_column='target')
import numpy as np from catboost import Pool # Create a NumPy array X = np.array([[0, 1], [2, 3], [4, 5]]) y = np.array([1, 0, 1]) # Create a pool object from the array train_data = Pool(data=X, label=y)These examples show how the CatBoost Pool class can be used to load and manage data for training machine learning models. The library used in this example is CatBoost.