<jupyter_start><jupyter_text>This is all duplicated from the Basic Regression package, so the confirmation that it works has been omitted<jupyter_code>KRR_Linear = KernelRidge(alpha=1.0, kernel='linear')
KRR_Linear.fit(inputs_train, targets_train)
#Note that RBF is the 
KRR_RBF = KernelRidge(alpha=1.0, kernel='rbf')
KRR_RBF.fit(inputs_train, targets_train)
print(KRR_Linear.score(inputs_test,targets_test))
print(KRR_RBF.score(inputs_test,targets_test))
print(KRR_Linear.predict(inputs_test)[0:6,0])
print(KRR_RBF.predict(inputs_test)[0:6,0])
print(targets_test[0:6,0])<jupyter_output>-3.7202860915132057
0.24949152447285416
{'fraction_non_alphanumeric': 0.11980830670926518, 'fraction_numerical': 0.07667731629392971, 'mean_word_length': 11.057692307692308, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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': '508303', 'license': 'CC0-1.0', 'n_tokens_mistral': 272, 'n_tokens_neox': 247, 'n_words': 38, 'path': '09_Kernel_Ridge_Regression.ipynb', 'repo_name': 'MatLaradeAcenet/ML_Parallel_School', 'size': '626'}