微调格式

docs/3.md

@@ -24,9 +24,9 @@ Linear Discriminant Analysis（线性判别分析）([`discriminant_analysis.Lin
 
 实现方式在 [`discriminant_analysis.LinearDiscriminantAnalysis.transform`](https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform) 中。关于维度的数量可以通过 `n_components` 参数来调节。 值得注意的是，这个参数不会对 [`discriminant_analysis.LinearDiscriminantAnalysis.fit`](https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.fit) 或者 [`discriminant_analysis.LinearDiscriminantAnalysis.predict`](https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict) 产生影响。
 
-示例:
-
-[Comparison of LDA and PCA 2D projection of Iris dataset](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-lda-py): 在 Iris 数据集对比 LDA 和 PCA 之间的降维差异
+>示例:
+>
+>[Comparison of LDA and PCA 2D projection of Iris dataset](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-lda-py): 在 Iris 数据集对比 LDA 和 PCA 之间的降维差异
 
 ## 1.2.2\. LDA 和 QDA 分类器的数学公式