python - Sklearn - 使用交叉验证的价格预测
问题描述
这是我的代码:
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import train_test_split
%matplotlib inline
boston_properties = load_boston()
l_distance = boston_properties['data'][:, np.newaxis, 7]
linreg = LinearRegression()
X_train, X_test, y_train, y_test = train_test_split(l_distance, boston_properties['target'], test_size = 0.3)
y_pred = cross_val_predict(linreg, l_distance, boston_properties.target, cv=5)
plt.scatter(X_test, y_test, color='black')
plt.plot(X_test, y_pred, color='blue', linewidth=2)
plt.show()
print(y_pred.shape)
我收到的错误如下:
ValueError: x 和 y 必须具有相同的第一维,但具有形状 (152, 1) 和 (506,)
我怎样才能使这项工作?
解决方案
你做了一个train_test_split
,但你没有用它来训练模型。然后你对整个训练数据进行预测,并将其与y_test
. 这是没有意义的。请改用这些行:
l_distance = boston_properties['data'][:, np.newaxis, 7]
linreg = LinearRegression()
X_train, X_test, y_train, y_test = train_test_split(l_distance,
boston_properties['target'], test_size = 0.3) # now you have a train/test set
y_pred = cross_val_predict(linreg, X_train, y_train, cv=5)
plt.scatter(X_train, y_train, color='black')
plt.plot(X_train, y_pred, color='blue', linewidth=2)
plt.show()
编辑:您也可以使用这条线通过您的点制作一条直线:
plt.scatter(X_train, y_train, color='black')
plt.plot([X_train[np.argmin(X_train)], X_train[np.argmax(X_train)]],
[y_pred[np.argmin(X_train)], y_pred[np.argmax(X_train)]],
color='blue')
plt.show()