<jupyter_start><jupyter_text>Resulting score on kaggle is 75,6%<jupyter_code>#Measure mean training time
measures = 100
result= []
Sum = 0
for i in range(measures):
    import time
    start = time.time()
    test = clf.fit(x_train, y_train)
    finish = time.time()
    result = finish-start
    Sum = Sum+result
    

mean = Sum/measures
print('Total time elapsed: {time}' .format(time = Sum))
print('Average time elapsed per measure: {time}'.format(time = mean))<jupyter_output>Total time elapsed: 0.20447230339050293
Average time elapsed per measure: 0.0020447230339050294<jupyter_text>Lets see if it can still be improved.<jupyter_code>from sklearn.model_selection import RandomizedSearchCV<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10206896551724139, 'fraction_numerical': 0.06206896551724138, 'mean_word_length': 5.205128205128205, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '340556', 'license': 'Apache-2.0', 'n_tokens_mistral': 282, 'n_tokens_neox': 244, 'n_words': 65, 'path': 'Titanic_logistic_regression.ipynb', 'repo_name': 'RickVM/Kaggle-Titanic', 'size': '725'}