<jupyter_start><jupyter_text>XGBoost<jupyter_code>import xgboost
from xgboost import XGBRegressor
# use xgboost with [ ]
xgboost_regressor = XGBRegressor(n_estimators=3, max_depth=2, reg_lambda=0, min_split_loss=1, learning_rate=0.7)
xgboost_regressor.fit(features,labels)
xgboost_regressor.score(features, labels)
utils.plot_regressor(xgboost_regressor, features, labels)
xgboost_regressor.predict(features)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0958904109589041, 'fraction_numerical': 0.0136986301369863, 'mean_word_length': 13.161290322580646, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, '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': '192668', 'license': 'Apache-2.0', 'n_tokens_mistral': 179, 'n_tokens_neox': 169, 'n_words': 22, 'path': 'ensemble_methods/XGBoost.ipynb', 'repo_name': 'radwaashraf680/winutils', 'size': '438'}