<jupyter_start><jupyter_text>Adhafera:![](adhafera/rf/redshift.png)<jupyter_code>errors = abs(pred - real)
print('Mean Absolute Error:', round(mean(errors), 2), 'degrees.')
mape = 100 * (errors / real)
accuracy = 100 - mean(mape)
print('Accuracy:', round(accuracy, 2), '%.')
plot_hm(real, pred, show_only=True)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.19411764705882353, 'fraction_numerical': 0.023529411764705882, 'mean_word_length': 7.973684210526316, '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': '353384', 'license': 'MIT', 'n_tokens_mistral': 150, 'n_tokens_neox': 134, 'n_words': 22, 'path': 'jupyter/notebooks/rf_redshift.ipynb', 'repo_name': 'MLRG-CEFET-RJ/parsl-ml-workflow', 'size': '340'}