Python 3.x ValueError:应为2D数组，而应为1D数组。线性回归的Python函数

我不确定如何解决此错误消息。我希望有人能帮忙。谢谢

import numpy as np
from sklearn import linear_model
from sklearn.model_selection import train_test_split


def desired_marketing_expenditure(x_train_marketing_expenditure, y_train_units_sold, x_test_units_sold):

    X_train, X_test, y_train, y_test = train_test_split(x_train_marketing_expenditure, y_train_units_sold, test_size=0.4, random_state=101)
    lm = linear_model.LinearRegression()
    lm.fit(X_train,y_train)
    print(lm.intercept_)
    print(lm.coef_)

    #predictions = lm.predict(x_test_units_sold)

print(desired_marketing_expenditure([300000, 200000, 400000, 300000, 100000],[60000, 50000, 90000, 80000, 30000],60000))

OUT:ValueError:应为2D数组，但改为1D数组： 数组=[400000 200000 300000]。

---

使用数组重塑数据。如果数据具有单个特征或数组，则重塑（-1,1）。如果数据包含单个样本，则重塑（1，-1）。

尝试将

X\u序列

重塑为

（-1,1）

，如错误中所述

import numpy as np
from numpy import array
from sklearn import linear_model
from sklearn.model_selection import train_test_split


def desired_marketing_expenditure(x_train_marketing_expenditure, y_train_units_sold, x_test_units_sold):

    X_train, X_test, y_train, y_test = train_test_split(x_train_marketing_expenditure, y_train_units_sold, test_size=0.4, random_state=101)
    lm = LinearRegression()
    X_train=array(X_train).reshape(-1,1)
    lm.fit(X_train,y_train)
    print(lm.intercept_)
    print(lm.coef_)

    #predictions = lm.predict(x_test_units_sold)

print(desired_marketing_expenditure([300000, 200000, 400000, 300000, 100000],[60000, 50000, 90000, 80000, 30000],60000))

输出：

13333.333333333343
[0.2]
None

---

期望向量。在numpy中，向量是垂直数组

[[1]，[2]]

而不是

[1,2]

import numpy as np
from sklearn import linear_model
from sklearn.model_selection import train_test_split


def desired_marketing_expenditure(x_train_marketing_expenditure, y_train_units_sold, x_test_units_sold):

    X_train, X_test, y_train, y_test = train_test_split(x_train_marketing_expenditure, y_train_units_sold, test_size=0.4, random_state=101)
    lm = linear_model.LinearRegression()
    lm.fit(X_train,y_train)
    print(lm.intercept_)
    print(lm.coef_)

    #predictions = lm.predict(x_test_units_sold)

print(desired_marketing_expenditure([[300000], [200000], [400000], [300000], [100000]],[[60000], [50000], [90000], [80000], [30000]], [[60000]]))

---

注意：不能转置1D数组

np.array（[1,2,3]）。T

与

np.array（[1,2,3]）

相同，因为转置需要第二个轴。您可以添加一个额外的轴并如下转置：

np.array（[1,2,3]）[np.newaxis].T

与

np.array（[1]、[2]、[3]]）

相同

[13333.33333333]
[[0.2]]
None