<jupyter_start><jupyter_text>Utilities  Configuring LoggingEvalML uses [the standard Python logging package](https://docs.python.org/3/library/logging.html). Default logging behavior prints WARNING level logs and above (ERROR and CRITICAL) to stdout. To configure different behavior, please refer to the Python logging documentation.To see up-to-date feedback as `AutoMLSearch` runs, use the argument `verbose=True` when instantiating the object. This will temporarily set up a logging object to print INFO level logs and above to stdout, as well as display a graph of the best score over pipeline iterations.  System InformationEvalML provides a command-line interface (CLI) tool prints the version of EvalML and core dependencies installed, as well as some basic system information. To use this tool, just run `evalml info` in your shell or terminal. This could be useful for debugging purposes or tracking down any version-related issues.<jupyter_code>!evalml info<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.05215646940822467, 'fraction_numerical': 0.0010030090270812437, 'mean_word_length': 5.6092715231788075, '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': '576666', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 240, 'n_tokens_neox': 237, 'n_words': 129, 'path': 'docs/source/user_guide/utilities.ipynb', 'repo_name': 'freddyaboulton/evalml', 'size': '997'}