在简单回归模型中使用预测功能时出错（形状未对齐）

Question

任何人都可以帮助我解决上述错误。我实际上在机器学习中使用的简单回归模型中使用了预测函数，但出现了一个错误。我已经使用了重塑功能来转换测试和训练数据，并相应地使用了它们。执行的代码是：-

   import pandas  as pd
   from numpy import *
   import matplotlib.pyplot as plt
   dataset=pd.read_csv("Salary_Data.csv")
   X=dataset.iloc[:,0].values
   X
   Y=dataset.iloc[:,1].values
   dataset.isnull()
   from sklearn.impute import SimpleImputer
   imputer=SimpleImputer(missing_values="NaN",strategy="mean",verbose=0)
   from sklearn.model_selection import train_test_split
   X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=1/3,random_state=0)
   x_train=X_train.reshape(1,-1)
   X_train
   x_train
   y_train=Y_train.reshape(1,-1)
   Y_test
   from sklearn.linear_model import LinearRegression
   regressor=LinearRegression()
   X_train
   LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)
   x_test=X_test.reshape(-1,1)
   x_test
   y_test=Y_test.reshape(-1,1)
   y_test
   x_train
   regressor.fit(x_train,y_train)
   x_test
   y_pred=regressor.predict(x_test)

错误是：

ValueError                                Traceback (most recent call last)
<ipython-input-42-350ddd5e0bb0> in <module>()
----> 1 y_pred=regressor.predict(x_test)

2 frames
/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py in safe_sparse_dot(a, b, dense_output)
    140         return ret
    141     else:
--> 142         return np.dot(a, b)
    143 
    144 

<__array_function__ internals> in dot(*args, **kwargs)

ValueError: shapes (10,1) and (20,20) not aligned: 1 (dim 1) != 20 (dim 0) 

Answer 1

模型的尺寸是错误的（即，您已用（1，-1）而不是（-1,1）进行了整形]

更改以下几行

x_train=X_train.reshape(1,-1) // your code Change to bellow code
x_train=X_train.reshape(-1,1) 
...
y_train=Y_train.reshape(1,-1)// your code Change to bellow code
y_train=Y_train.reshape(-1,1)

我希望这对您有用