<jupyter_start><jupyter_text>03 Error Estimation for Linear Regression and 3NN<jupyter_code>%load_ext autoreload
%autoreload 2
%matplotlib inline
import pandas as pd
import numpy as np

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline

from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor

from sklearn.model_selection import cross_val_score<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.03777335984095427, 'fraction_numerical': 0.007952286282306162, 'mean_word_length': 8.163636363636364, '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': '30258', 'license': 'MIT', 'n_tokens_mistral': 160, 'n_tokens_neox': 151, 'n_words': 45, 'path': 'ai1/labs/AI1_03.ipynb', 'repo_name': 'derekgbridge/artificial_intelligence', 'size': '503'}