Sklearn PCA是pca.components_的负载吗? [英] Sklearn PCA is pca.components_ the loadings?

问题描述

Sklearn PCA是否为pca.components_?我很确定，但是我正在尝试研究论文，并且从加载量中得到了不同的结果.我在sklearn文档中找不到它.

Sklearn PCA is pca.components_ the loadings? I am pretty sure it is, but I am trying to follow along a research paper and I am getting different results from their loadings. I can't find it within the sklearn documentation.

推荐答案

pca.components_是将数据投影到的空间的正交基础.它的形状为(n_components, n_features).如果要保留具有100个样本和50个维度(也称为要素)的数据集的前三个成分(例如进行3D散点图)，则pca.components_将具有形状(3, 50).

pca.components_ is the orthogonal basis of the space your projecting the data into. It has shape (n_components, n_features). If you want to keep the only the first 3 components (for instance to do a 3D scatter plot) of a datasets with 100 samples and 50 dimensions (also named features), pca.components_ will have shape (3, 50).

我认为您所说的载荷"是每个样本投影到组件所跨越的向量空间中的结果.可以通过在调用pca.fit(X_train)之后调用pca.transform(X_train)获得这些.结果将具有形状(n_samples, n_components)，在我们之前的示例中为(100, 3).

I think what you call the "loadings" is the result of the projection for each sample into the vector space spanned by the components. Those can be obtained by calling pca.transform(X_train) after calling pca.fit(X_train). The result will have shape (n_samples, n_components), that is (100, 3) for our previous example.

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