<jupyter_start><jupyter_text>Testing Phase<jupyter_code>y_pred_test = predict(X_test,prior_probabilities,mean,variance,classes)
def testing_accuracy(y_test, y_pred_test):
    test_accuracy = np.sum(y_test == y_pred_test) / len(y_test)
    return test_accuracy

testing_accuracy(Y_test,y_pred_test)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10091743119266056, 'fraction_numerical': 0.0, 'mean_word_length': 10.714285714285714, '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': '461101', 'license': 'MIT', 'n_tokens_mistral': 132, 'n_tokens_neox': 129, 'n_words': 13, 'path': 'NaiveBayes.ipynb', 'repo_name': 'aryachiranjeev/Scratch-modules', 'size': '327'}