python 线性回归异常值_Python Sklearn线性回归值错误

Ive been trying out Linear Regression using sklearn. Sometime I get a value error, sometimes it works fine. Im not sure which approach to use.

Error Message is as follows:

Traceback (most recent call last):

File "", line 1, in

File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/base.py", line 512, in fit

y_numeric=True, multi_output=True)

File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/utils/validation.py", line 531, in check_X_y

check_consistent_length(X, y)

File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/utils/validation.py", line 181, in check_consistent_length

" samples: %r" % [int(l) for l in lengths])

ValueError: Found input variables with inconsistent numbers of samples: [1, 200]

The code is something like this:

import pandas as pd

from sklearn.linear_model import LinearRegression

data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0);

x = data['TV']

y = data['Sales']

lm = LinearRegression()

lm.fit(x,y)

Please help me out. I am a student, trying to pick up on Machine Learning basics.

解决方案

lm.fit expects X to be a

numpy array or sparse matrix of shape [n_samples,n_features]

Your x has shape:

In [6]: x.shape

Out[6]: (200,)

Just use:

lm.fit(x.reshape(-1,1) ,y)

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