<jupyter_start><jupyter_text><jupyter_code>from xgboost import XGBClassifier
import numpy as np
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
# https://stackoverflow.com/questions/57986259/multiclass-classification-with-xgboost-classifier
# !unzip -q original.zip
model = XGBClassifier (learning_rate=0.5, objective='multi:softmax')
big5 = pd.read_csv ('big5.csv', sep='\t')
big5.head()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10074626865671642, 'fraction_numerical': 0.024253731343283583, 'mean_word_length': 7.661290322580645, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '573159', 'license': 'Unlicense', 'n_tokens_mistral': 186, 'n_tokens_neox': 178, 'n_words': 37, 'path': 'Google_CoLab_XGB_Big5.ipynb', 'repo_name': 'LeonVillanueva/CoLab', 'size': '536'}