[英]How to perform multivariable linear regression with scikit-learn?
Forgive my terminology, I'm not an ML pro. 原谅我的术语,我不是ML专业人士。 I might use the wrong terms below.
我可能在下面使用错误的术语。
I'm trying to perform multivariable linear regression. 我正在尝试执行多变量线性回归。 Let's say I'm trying to work out user gender by analysing page views on a web site.
假设我正在尝试通过分析网站上的页面浏览量来确定用户性别。
For each user whose gender I know, I have a feature matrix where each row represents a web site section, and the second element whether they visited it, eg: 对于我认识的每个性别的用户,我都有一个特征矩阵,其中每一行代表一个网站部分,第二个元素是他们是否访问过网站部分,例如:
male1 = [
[1, 1], # visited section 1
[2, 0], # didn't visit section 2
[3, 1], # visited section 3, etc
[4, 0]
]
So in scikit, I am building xs
and ys
. 因此,在scikit中,我正在构建
xs
和ys
。 I'm representing a male as 1, and female as 0. 我代表男性为1,女性为0。
The above would be represented as: 以上将表示为:
features = male1
gender = 1
Now, I'm obviously not just training a model for a single user, but instead I have tens of thousands of users whose data I'm using for training. 现在,我显然不仅在为单个用户训练模型,而且还有数以万计的用户正在使用我的数据进行训练。
I would have thought I should create my xs
and ys
as follows: 我本以为应该按如下方式创建
xs
和ys
:
xs = [
[ # user1
[1, 1],
[2, 0],
[3, 1],
[4, 0]
],
[ # user2
[1, 0],
[2, 1],
[3, 1],
[4, 0]
],
...
]
ys = [1, 0, ...]
scikit doesn't like this: scikit不喜欢这样:
from sklearn import linear_model
clf = linear_model.LinearRegression()
clf.fit(xs, ys)
It complains: 它抱怨:
ValueError: Found array with dim 3. Estimator expected <= 2.
How am I supposed to supply a feature matrix to the linear regression algorithm in scikit-learn? 我应该如何在scikit-learn中为线性回归算法提供特征矩阵?
You need to create xs
in a different way. 您需要以其他方式创建
xs
。 According to the docs : 根据文档 :
fit(X, y, sample_weight=None)
Parameters:
参数:
X : numpy array or sparse matrix of shape [n_samples, n_features] Training data y : numpy array of shape [n_samples, n_targets] Target values sample_weight : numpy array of shape [n_samples] Individual weights for each sample
Hence xs
should be a 2D array with as many rows as users and as many columns as web site sections. 因此,
xs
应该是一个2D数组,其行数与用户数相同,列数与网站部分相同。 You defined xs
as a 3D array though. 您将
xs
定义为3D数组。 In order to reduce the number of dimensions by one you could get rid of the section numbers through a list comprehension: 为了将尺寸数减少一,您可以通过列表理解来摆脱节号:
xs = [[visit for section, visit in user] for user in xs]
If you do so, the data you provided as an example gets transformed into: 如果这样做,您作为示例提供的数据将转换为:
xs = [[1, 0, 1, 0], # user1
[0, 1, 1, 0], # user2
...
]
and clf.fit(xs, ys)
should work as expected. 和
clf.fit(xs, ys)
应该可以正常工作。
A more efficient approach to dimension reduction would be that of slicing a NumPy array: 减少维度的更有效方法是切片NumPy数组:
import numpy as np
xs = np.asarray(xs)[:,:,1]
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