pca.explained_variance_ratio_()代码怎么写
时间: 2023-07-16 20:11:27 浏览: 63
假设已经使用PCA对数据进行了降维,可以使用`explained_variance_ratio_()`方法获取每个主成分解释的方差比例,代码如下:
```python
from sklearn.decomposition import PCA
# 假设已经使用PCA对数据进行了降维,存储在pca对象中
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
# 获取每个主成分解释的方差比例
var_ratio = pca.explained_variance_ratio_
print(var_ratio)
```
`var_ratio`将包含每个主成分解释的方差比例,它们按照降序排列。例如,如果有5个主成分,则`var_ratio`可能是`[0.4, 0.3, 0.2, 0.05, 0.05]`,表示第一个主成分解释了总方差的40%,第二个解释了30%,以此类推。
相关问题
pca.explained_
PCA的explained_variance_ratio_属性是一个数组,它表示每个主成分解释的方差比例。这个属性可以用来判断每个主成分所包含的信息量。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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[ .reference_list ]
explained_variance_ratio = pca.explained_variance_ratio_
This variable stores the explained variance ratio of each principal component in the PCA analysis. It is an array of length equal to the number of principal components computed in the analysis. The value of each element represents the proportion of the total variance in the data that is explained by the corresponding principal component. The sum of all elements in the array is equal to 1.0. This information can be used to determine the number of principal components needed to capture a certain percentage of the total variance in the data. For example, if the first two principal components have an explained variance ratio of 0.5 and 0.3, respectively, then these two components capture 80% of the total variance in the data.