<jupyter_start><jupyter_text>https://scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html<jupyter_code>from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
parameters = {'clf_svc__C':[1,10,1e3,1e4,1e5],"clf_svc__gamma":[1e-3,5e-4,1e-4,5e-3]}
pipe1=Pipeline([('scaler',StandardScaler()),('clf_svc',SVC())])
pipe1
abc=GridSearchCV(pipe1,parameters)
abc.fit(x,y)
abc.best_estimator_
abc.cv_results_<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16279069767441862, 'fraction_numerical': 0.042283298097251586, 'mean_word_length': 12.542857142857143, 'pattern_counts': {'":': 1, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, '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': '601517', 'license': 'MIT', 'n_tokens_mistral': 212, 'n_tokens_neox': 206, 'n_words': 16, 'path': 'JupyterCode/ML/SupportVectorMachine_code.ipynb', 'repo_name': 'gokulyc/python123', 'size': '473'}