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선생님! 강의 잘 보고 있습니다.!

해결되었습니다! 감사합니다 선생님!

무슨 문제인지 모르겠어요

import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline&nbsp;
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
import numpy as np
from sklearn.metrics import mean_squared_error, r2_score
%matplotlib inline

def true_fun(x):
&nbsp; &nbsp; return np.cos(1.5 *np.pi * x)&nbsp;
# random 값으로 구성된 X값에 대해 Cosine 변환값을 반환&nbsp;&nbsp;


np.random.seed(0)
n_samples=30
x=np.sort(np.random.rand(n_samples))
y=true_fun(x)+np.random.randn(n_samples)*0.1
plt.figure(figsize=(14,5))

degrees=[1,4,15]

for i in range(len(degrees)):
&nbsp; &nbsp; ax=plt.subplot(1,len(degrees),i+1)
&nbsp; &nbsp; plt.setp(ax,xticks=(),yticks=())
&nbsp; &nbsp;&nbsp;
&nbsp; &nbsp; # 개별 degree별로 polynomial 변환합니다.
&nbsp; &nbsp; polynomial_features=PolynomialFeatures(degree=degrees[i],include_bias=False)&nbsp; &nbsp;# 상수항 포함 X
&nbsp; &nbsp; linear_regression=LinearRegression()
&nbsp; &nbsp; pipe=Pipeline([('polynomial_features',PolynomialFeatures),('linear_regression',LinearRegression)])
&nbsp; &nbsp; pipe.fit(x.reshape(-1,1),y)
&nbsp; &nbsp;&nbsp;
&nbsp; &nbsp; scores=cross_val_score(pipe,x.reshape(-1,1),y,scoring='neg_mean_squared_error',cv=10)
&nbsp; &nbsp;&nbsp;
&nbsp; &nbsp; #pipeline을 구성하는 세부 객체를 접근하는 named_steps['객체명']을 이용해 회귀계수 추출
&nbsp; &nbsp; coefficients=pipe.named_steps['linear_regression'].coef_
&nbsp; &nbsp;&nbsp;
&nbsp; &nbsp; print("\n Degree {0} 회귀 계수는 {1} 입니다.".format(degrees[i],np.round(coefficients,2)))
&nbsp; &nbsp; print('\n Degree {0} MSE는 {1}입니다.'.format(degrees[i],-1*np.mean(scores)))
&nbsp; &nbsp;&nbsp;
&nbsp; &nbsp; # 0부터 1까지 테스트 데이터 세트를 100개로 나눠 예측을 수행합니다.&nbsp;
&nbsp; &nbsp; # 테스트 데이터 세트에 회귀 예측을 수행하고 예측 곡선과 실제 곡선을 그려서 비교합니다.
&nbsp; &nbsp;&nbsp;
&nbsp; &nbsp; X_test=np.linspace(0,1,100)
&nbsp; &nbsp; # 예측값 곡선
&nbsp; &nbsp; plt.plot(X_test,pipe.predict(X_test[:,np.newaxis]),label="Model")
&nbsp; &nbsp; # 실제값 곡선
&nbsp; &nbsp; plt.plot(X_test.true_fun(X_test),'--',label="True function")
&nbsp; &nbsp; plt.scatter(X,y,edgecolor='b',s=20,label="Samples")
&nbsp; &nbsp; plt.xlabel("X");plt.ylabel("y");plt.xlim((0,1));plt.ylim((-2,2));plt.legend(loc='best') # 최적의 위치에 범례 위치시킴
&nbsp; &nbsp; plt.title("Degree {}\nMSE={:.2e}(+/-{:.2e})".formate(degrees[i],-scores.mean(),scores.std()))
&nbsp; &nbsp;&nbsp;
plt.show()

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