1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | import matplotlib.pylab as plt import numpy as np from sklearn.linear_model import LinearRegression from sklearn import datasets # ´ç´¢º´ µ¥ÀÌÅÍ ¼¼Æ®¸¦ ÀûÀçÇÑ´Ù. diabetes = datasets.load_diabetes() # 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, random_state=0) model = LinearRegression() model.fit(X_train, y_train) # Å×½ºÆ® µ¥ÀÌÅÍ·Î ¿¹ÃøÇغ¸ÀÚ. y_pred = model.predict(X_test) # ½ÇÁ¦ µ¥ÀÌÅÍ¿Í ¿¹Ãø µ¥ÀÌÅ͸¦ ºñ±³Çغ¸ÀÚ. plt.plot(y_test, y_pred, '.') # Á÷¼±À» ±×¸®±â À§ÇÏ¿© ¿Ïº®ÇÑ ¼±Çü µ¥ÀÌÅ͸¦ »ý¼ºÇÑ´Ù. x = np.linspace(0, 330, 100) y = x plt.plot(x, y) plt.show() | cs |