image_output_1.png
from catboost import CatBoostClassifier

import pandas as pd

import pickle

valid_ratio = 0.2

X = pickle.load(open('/kaggle/input/embedder/train_embedding', 'rb'))
y = pd.read_csv('/kaggle/input/embedder/preprocessed_train.csv').target_relabeled.values

valid_size = int(y.shape[0] * valid_ratio)

in_features = X.shape[1]

from catboost import CatBoostClassifier
catboostclassifier = CatBoostClassifier(iterations=20)
catboostclassifier.fit(X[:-valid_size], y[:-valid_size], eval_set=(X[-valid_size:], y[-valid_size:]), logging_level='Silent')