Question

我正在训练数据虹膜。
我尝试了一些线性回归，但是我使用的其中一个版本似乎无效。
有人可以解释一下为什么吗？
无效的表达式：

Y4 = iris_versicolor['sepal_width']
X4 = iris_versicolor[['petal_width']]
X4 = sm.add_constant(X4)
moindreCareCas4 = sm.OLS(Y4, X4).fit() # OLS = Ordinary Least Square 
a4,b4 = moindreCareCas4.params['petal_width'],moindreCareCas4.params['intercept']

print(moindreCareCas4.params)

plt.plot(iris_versicolor.sepal_width,iris_versicolor.petal_width, "o")
plt.plot(np.arange(4),[a4*x+b4 for x in np.arange(4)])
plt.xlabel("petal_width")
plt.ylabel("sepal_width")
plt.show()

线性回归目前在点云之上

有效表达：

import matplotlib.pyplot as plt
import numpy as np
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score

# Create linear regression object
regr = linear_model.LinearRegression()

# Train the model using the training sets
regr.fit(iris_versicolor[['sepal_width']], iris_versicolor[['petal_width']])

# Make predictions using the testing set
iris_versicolor_petal_width_pred = regr.predict(iris_versicolor[['sepal_width']])

# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean squared error
print('Mean squared error: %.2f'
      % mean_squared_error(iris_versicolor[['petal_width']], iris_versicolor_petal_width_pred))
# The coefficient of determination: 1 is perfect prediction
print('Coefficient of determination: %.2f'
      % r2_score(iris_versicolor[['petal_width']], iris_versicolor_petal_width_pred))

# Plot outputs
plt.scatter(iris_versicolor[['sepal_width']], iris_versicolor[['petal_width']],  color='black')
plt.plot(iris_versicolor[['sepal_width']], iris_versicolor_petal_width_pred, color='blue', linewidth=3)

plt.xticks(())
plt.yticks(())

plt.show()

线性回归在点云中，就像必须要那样。

谢谢！

编码线性回归时遇到问题

0 个答案: