#
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# LAB 14-2 µ¥ÀÌÅÍ 80%·Î ÇнÀÇÏ¿© ¿¹ÃøÇÑ °á°ú¿Í ½ÇÁ¦ µ¥ÀÌÅÍ ºñ±³, 382ÂÊ
#
import numpy as np
from sklearn import linear_model # scikit-learn ¸ðµâÀ» °¡Á®¿Â´Ù
from sklearn import datasets
import matplotlib.pyplot as plt
diabetes = datasets.load_diabetes()
regr = linear_model.LinearRegression()
# ÇнÀ µ¥ÀÌÅÍ¿Í Å×½ºÆ® µ¥ÀÌÅ͸¦ ºÐ¸®ÇÑ´Ù.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(diabetes.data,
diabetes.target,
test_size=0.2)
regr.fit(X_train, y_train)
print(regr.coef_, regr.intercept_)
y_pred = regr.predict(X_test)
plt.scatter(y_pred, y_test)
plt.show()