<jupyter_start><jupyter_text>Decision Tree<jupyter_code>from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(criterion = 'entropy', random_state = 42)
dt.fit(X_train, Y_train)
dt_pred_train = dt.predict(X_train)
t_pred_train = dt.predict(X_train)
print('Training Set Evaluation F1-Score=>',f1_score(Y_train,dt_pred_train,average=None))
from sklearn.svm import SVC
classifier = SVC(kernel='rbf', random_state=42)
classifier.fit(X_train, Y_train)
Y_pred = classifier.predict(X_test)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11444652908067542, 'fraction_numerical': 0.01125703564727955, 'mean_word_length': 10.608695652173912, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 6, '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': '590932', 'license': 'MIT', 'n_tokens_mistral': 208, 'n_tokens_neox': 197, 'n_words': 31, 'path': 'model/CropAI.ipynb', 'repo_name': 'PratikT112/AltCrop-9', 'size': '533'}