问题描述
我想要python中各个变量和主要成分之间的相关性.我在sklearn中使用PCA.我不理解分解数据后如何获得加载矩阵?我的代码在这里.
I want the correlations between individual variables and principal components in python.I am using PCA in sklearn. I don't understand how can I achieve the loading matrix after I have decomposed my data? My code is here.
iris = load_iris()
data, y = iris.data, iris.target
pca = PCA(n_components=2)
transformed_data = pca.fit(data).transform(data)
eigenValues = pca.explained_variance_ratio_
http://scikit-learn.org/stable/modules/generation/sklearn .decomposition.PCA.html 没有提及如何实现.
推荐答案
我认为@RickardSjogren在描述特征向量，而@BigPanda在进行加载.有很大的不同:加载与特征向量在PCA中:何时使用一个或另一个?.
I think that @RickardSjogren is describing the eigenvectors, while @BigPanda is giving the loadings. There's a big difference: Loadings vs eigenvectors in PCA: when to use one or another?.
我使用loadings方法.
载荷与多元线性回归中的系数更相似.我在这里不使用.T，因为在上面链接的PCA类中，组件已经转置了. numpy.linalg.svd生成u, s, and vt，其中vt是Hermetian转置，因此您首先需要使用vt.T返回到v.
Loadings, as given by pca.components_ * np.sqrt(pca.explained_variance_), are more analogous to coefficients in a multiple linear regression. I don't use .T here because in the PCA class linked above, the components are already transposed. numpy.linalg.svd produces u, s, and vt, where vt is the Hermetian transpose, so you first need to back into v with vt.T.
还有一个重要的细节:sklearn.PCA中组件和负载的符号(正/负)可能与R之类的包装有所不同. 此处的更多信息:
There is also one other important detail: the signs (positive/negative) on the components and loadings in sklearn.PCA may differ from packages such as R. More on that here:
在sklearn.decomposition.PCA中，components_为何为负?/a>.
In sklearn.decomposition.PCA, why are components_ negative?.
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