Python多元线性回归无法绘制

Question

我正在尝试运行多元线性回归，但在绘制结果时遇到了麻烦。我正在尝试绘制3D图，我得到此输出ValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (4,) and requested shape (34,)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train,y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X.iloc[:, 0], X.iloc[:, 1], Y)
ax.plot(X.iloc[:, 0], X.iloc[:, 1], y_pred, color='red')

ax.set_xlabel('Annual Income (k$)')
ax.set_ylabel('Age')
ax.set_zlabel('Spending Score')
plt.show()

编辑：

编辑2：

Answer 1

绘图命令应为：

ax.plot(X_test.iloc[:, 0], X_test.iloc[:, 1], y_pred, color='red')

因为y_pred仅包含子集X_test的y值，而不包含整个输入X的y个值。

用连接的线（ax.plot）进行绘制没有意义，因为输入数据可能没有以有意义的方式进行排序，并且即使对输入数据进行了排序也绝对没有对测试集进行排序。

我会这样绘制：

from sklearn.model_selection import train_test_split
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

# generate some data as an example
np.random.seed(1)
X = pd.DataFrame(np.random.uniform(size=(20, 2)))
Y = X[0] + 2*X[1] + np.random.normal(scale=0.2, size=(20))


X_train, X_test, y_train,y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X[0], X[1], Y, label='data')

for x0, x1, yt, yp in zip(X_test[0], X_test[1], y_test, y_pred):
    ax.plot([x0, x0], [x1, x1], [yt, yp], color='red')

ax.scatter(X_test[0], X_test[1], y_pred, color='red', marker='s', label='prediction') 

ax.set_xlabel('X0')
ax.set_ylabel('X1')
ax.set_zlabel('y')
ax.legend()
fig.show()

还有其他可视化方法。您可以使用np.meshgrid在网格上生成X值，并从预测变量中获取y值，然后使用plot_wireframe对其进行绘制，并使用垂直线绘制火车和测试数据以指示其与线框的垂直距离。这取决于有意义的数据。