linear model畫圖問題

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score, accuracy_score
from sklearn.preprocessing import StandardScaler
wine = datasets.load_wine()
x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.1, random_state=4)


# 建立一個線性回歸模型
regr = linear_model.LinearRegression(normalize=True)

# 將訓練資料丟進去模型訓練
regr.fit(x_train, y_train)

# 將測試資料丟進模型得到預測結果
y_pred = regr.predict(x_test)
print("Mean squared error: %.2f"
      % mean_squared_error(y_test, y_pred))
# 畫出回歸模型與實際資料的分佈
print(x_test.shape, y_test.shape)
plt.scatter(x_test, y_test,  color='black')
plt.plot(x_test, y_pred, color='blue', linewidth=3)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score, accuracy_score
from sklearn.preprocessing import StandardScaler
wine = datasets.load_wine()
x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.1, random_state=4)


# 建立一個線性回歸模型
regr = linear_model.LinearRegression(normalize=True)

# 將訓練資料丟進去模型訓練
regr.fit(x_train, y_train)

# 將測試資料丟進模型得到預測結果
y_pred = regr.predict(x_test)
print("Mean squared error: %.2f"
      % mean_squared_error(y_test, y_pred))
# 畫出回歸模型與實際資料的分佈
print(x_test.shape, y_test.shape)
plt.scatter(x_test, y_test,  color='black')
plt.plot(x_test, y_pred, color='blue', linewidth=3)
plt.show()

linear model畫圖問題

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