<jupyter_start><jupyter_text>Let's check out SVM<jupyter_code>print("Train \t\t\t Test")
for train_index, test_index in kf.split(X):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    clf = svm.SVC(gamma=0.001,C=0.1,class_weight="balanced",kernel="linear")
    clf.fit(X_train, y_train) 
    y_ = clf.predict(X_test)

    y_pred = clf.predict(X_train)
    
    print(accuracy_score(y_train,y_pred),"\t",accuracy_score(y_test,y_))

confusion_matrix(y_test,y_,labels=[1,2,3])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15714285714285714, 'fraction_numerical': 0.01607142857142857, 'mean_word_length': 5.925925925925926, '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': '488706', 'license': 'Apache-2.0', 'n_tokens_mistral': 253, 'n_tokens_neox': 244, 'n_words': 29, 'path': 'Logistic Regession.ipynb', 'repo_name': 'Mohsenselseleh/My-Projects', 'size': '560'}