sklearn PCA

PCA是指主成分分析（Principal Component Analysis）,是一种常用的降维算法。在sklearn库中，可以使用以下代码导入PCA模块：from sklearn.decomposition import PCA。 PCA模块提供了fit()方法来对数据进行降维，fit()方法是PCA算法中的训练步骤。由于PCA是无监督学习算法，所以fit()方法的参数y通常为None。在PCA模块中，还有一些重要的参数和属性，比如n_components、svd_solver、random_state、components_、explained_variance_和explained_variance_ratio_等。在使用PCA对手写数字数据集进行降维的案例中，可以使用以下代码导入需要的模块和库：from sklearn.decomposition import PCA from sklearn.ensemble import RandomForestClassifier as RFC from sklearn.model_selection import cross_val_score import matplotlib.pyplot as plt import pandas as pd import numpy as np。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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