使用碎石图选择成分
保留足够的解释方差
使用碎石图选择成分 是 CoddyKit 上的免费 Data Science Academy 课时。 这是第 4 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Data Science Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Data Science Academy 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
How Many to Keep?
PCA can hand you dozens of components, but you only want the useful few. The real skill is choosing how many to keep.
Meet the Scree Plot
A scree plot charts each component against the variance it explains, so you can see importance drop off at a glance.
Look for the Elbow
Variance falls fast then levels into a flat tail. The bend, called the elbow, marks where extra components stop paying off.
Plot the Ratios
Fit PCA with all components, then plot explained_variance_ratio_ to draw the curve and spot that elbow.
import matplotlib.pyplot as plt
plt.plot(pca.explained_variance_ratio_)Cumulative Variance
Add the ratios up as you go to get cumulative variance, showing the total information kept by the first k components.
import numpy as np
cum = np.cumsum(pca.explained_variance_ratio_)Pick a Threshold
A common rule is to keep enough components to reach a target, like 95 percent of total variance retained.
Let scikit-learn Choose
Pass a fraction as n_components and scikit-learn keeps just enough components to hit that explained-variance target.
from sklearn.decomposition import PCA
pca = PCA(n_components=0.95).fit(X)The Kaiser Rule
Another guide, the Kaiser rule, keeps components whose eigenvalue exceeds one, meaning they explain more than a single feature would.
Balance the Trade-Off
Fewer components mean simpler, faster models but more lost detail. Choosing k is always a trade-off between size and fidelity.
Validate Downstream
The best k is the one that helps your real task. Try a few values and compare model scores with cross-validation.
Beware Tiny Components
Components past the elbow often capture mostly noise. Keeping them rarely helps and can quietly hurt your model.
Quick Check
The scree plot points you to one telltale spot.
Recap
Use a scree plot, elbow, or a cumulative-variance threshold to keep just enough components, then validate k downstream. 🎯
常见问题解答
「使用碎石图选择成分」课时是免费的吗？
是的 — 「使用碎石图选择成分」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Data Science Academy 课程的其余内容，请升级到 CoddyKit PRO。 Data Science Academy 课程共包含 4 节课。
「使用碎石图选择成分」这节课中我会学到什么？
保留足够的解释方差 你通过在浏览器中直接运行的动手代码来练习 Data Science Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Data Science Academy 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Data Science Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 4 节课，共 4 节。
「使用碎石图选择成分」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Data Science Academy 课中编写并运行代码吗？
能。每节 Data Science Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。
此课程中的所有课时
- 特征过多的诅咒
- PCA 如何找出成分
- 先缩放，再拟合 PCA
- 使用碎石图选择成分