Python scikit 学习 pca.explained_variance_ratio_ cutoff [英] Python scikit learn pca.explained_variance_ratio_ cutoff

问题描述

在选择主成分数 (k) 时，我们选择 k 作为最小值，以便保留 99% 的方差.

When choosing the number of principal components (k), we choose k to be the smallest value so that for example, 99% of variance, is retained.

但是，在 Python Scikit 学习中，我不是 100% 确定 pca.explained_variance_ratio_ = 0.99 等于保留了 99% 的方差"?有谁能开导吗?谢谢.

However, in the Python Scikit learn, I am not 100% sure pca.explained_variance_ratio_ = 0.99 is equal to "99% of variance is retained"? Could anyone enlighten? Thanks.

• Python Scikit 学习 PCA 手册在这里

http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA

推荐答案

是的，您几乎是对的.pca.explained_variance_ratio_ 参数返回由每个维度解释的方差的向量.因此，pca.explained_variance_ratio_[i] 给出了仅由第 i+1 个维度解释的方差.

Yes, you are nearly right. The pca.explained_variance_ratio_ parameter returns a vector of the variance explained by each dimension. Thus pca.explained_variance_ratio_[i] gives the variance explained solely by the i+1st dimension.

您可能想要执行 pca.explained_variance_ratio_.cumsum().这将返回一个向量 x 使得 x[i] 返回由前 i+1 个维度解释的 累积 方差.

You probably want to do pca.explained_variance_ratio_.cumsum(). That will return a vector x such that x[i] returns the cumulative variance explained by the first i+1 dimensions.

import numpy as np
from sklearn.decomposition import PCA

np.random.seed(0)
my_matrix = np.random.randn(20, 5)

my_model = PCA(n_components=5)
my_model.fit_transform(my_matrix)

print my_model.explained_variance_
print my_model.explained_variance_ratio_
print my_model.explained_variance_ratio_.cumsum()

<小时>

[ 1.50756565  1.29374452  0.97042041  0.61712667  0.31529082]
[ 0.32047581  0.27502207  0.20629036  0.13118776  0.067024  ]
[ 0.32047581  0.59549787  0.80178824  0.932976    1.        ]

所以在我的随机玩具数据中，如果我选择 k=4，我将保留 93.3% 的方差.

So in my random toy data, if I picked k=4 I would retain 93.3% of the variance.

这篇关于Python scikit 学习 pca.explained_variance_ratio_ cutoff的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！