Python/Scikit-learn - 如何实际预测？

Question

我有以下 DataFrame，我称之为 main_frame:

               Value     Value      1lag      2lag      3lag      4lag 
Date                                                                     
2005-04-01  0.824427  0.892308  1.000000  0.000000  0.000000  0.000000   
2005-05-01  0.778626  0.953846  0.892308  1.000000  0.000000  0.000000   
2005-06-01  0.717557  1.000000  0.953846  0.892308  1.000000  0.000000   
2005-07-01  0.725191  0.000000  1.000000  0.953846  0.892308  1.000000   
2005-08-01  0.717557  1.000000  0.000000  1.000000  0.953846  0.892308   
2005-09-01  0.740458  0.861538  1.000000  0.000000  1.000000  0.953846   
2005-10-01  0.732824  0.877193  0.861538  1.000000  0.000000  1.000000   
2005-11-01  0.732824  1.000000  0.877193  0.861538  1.000000  0.000000   
2005-12-01  0.641221  1.000000  1.000000  0.877193  0.861538  1.000000   
2006-01-01  0.709924  0.614035  1.000000  1.000000  0.877193  0.861538   
2006-02-01  0.770992  0.649123  0.614035  1.000000  1.000000  0.877193

我构建了以下模型：

predictor=main_frame.iloc[:,1:]
target=main_frame.iloc[:,0]

model=LinearRegression()
model.fit(X=predictor,y=target)

我知道要进行预测，我现在应该使用 model.predict()，但是我很难理解预测函数的参数是如何工作的。我正在尝试使用：

prediction=model.predict(target)
print predict

但这总是让我出错，我相信我误解了与该论点相关的内容。

如何设置命令以使预测起作用？

编辑

我正在添加回溯

/Library/Python/2.7/site-packages/sklearn/utils/validation.py:386:     DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
DeprecationWarning)
Traceback (most recent call last):
File "/Users/file.py", line 61, in <module>
prediction=model.predict(target)
File "/Library/Python/2.7/site-packages/sklearn/linear_model/base.py", line 200, in predict
return self._decision_function(X)
File "/Library/Python/2.7/site-packages/sklearn/linear_model/base.py", line 185, in _decision_function
dense_output=True) + self.intercept_
File "/Library/Python/2.7/site-packages/sklearn/utils/extmath.py", line 184, in safe_sparse_dot
return fast_dot(a, b)
ValueError: shapes (1,127) and (144,) not aligned: 127 (dim 1) != 144 (dim 0)

编辑 2

尝试换句话说我的问题，以便更好地回答：

Considering the above model, how do I find out what is going to be the predicted value for the next period for the target variable?

Answer 1

您向预测函数传递了错误的参数。试试这个：

prediction=model.predict(predictor)
print prediction

请注意，模型已使用 "predictor" 变量进行训练。因此，您只能预测与 "predictor" 变量具有完全相同列数的数据。

Python/Scikit-learn - 如何实际预测？

Python/Scikit-learn - How to actually predict?

python

pandas

scikit-learn