<jupyter_start><jupyter_text>Classifier<jupyter_code>from Classifier import Classifier
the_classifier = Classifier()<jupyter_output><empty_output><jupyter_text>SVM<jupyter_code>c_svm, y_pred, distances = the_classifier.svm(X_train, y_train, X_test, kernel='linear', C=1)
_  = the_classifier.plot_confusion_matrix(y_test, y_pred, np.array(['no cancer','cancer']),normalize=True)
_ = the_classifier.roc(y_test, distances)<jupyter_output>Normalized confusion matrix
{'fraction_non_alphanumeric': 0.12121212121212122, 'fraction_numerical': 0.0021645021645021645, 'mean_word_length': 13.46875, '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': '580080', 'license': 'MIT', 'n_tokens_mistral': 177, 'n_tokens_neox': 174, 'n_words': 21, 'path': 'Classifier_interface.ipynb', 'repo_name': 'raym96/personal', 'size': '462'}