Python Sklearn 线性回归值误差

Question

我一直在尝试使用 sklearn 进行线性回归。有时我得到一个值错误，有时它工作正常。我不确定使用哪种方法。报错信息如下：

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  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]

代码是这样的：

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)

请帮帮我。我是一名学生，正在努力学习机器学习基础知识。

Answer 1

将您的 X 作为数据框而不是系列传递，您可以使用 [[]] "double brackets" 或 to_frame() 作为单个特征：

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']]

或者

x = data['TV'].to_frame()
y = data['Sales']
lm = LinearRegression()
lm.fit(x,y)

输出：

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

Answer 2

lm.fit 期望 X 成为

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

您的 x 形状：

In [6]: x.shape
Out[6]: (200,)

只需使用：

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

Python Sklearn 线性回归值误差

Python Sklearn Linear Regression Value Error

python

machine-learning

linear-regression

pandas

sklearn-pandas