岭回归和套索正则化
用惩罚项抑制过拟合
岭回归和套索正则化 是 CoddyKit 上的免费 Data Science Academy 课时。 这是第 2 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。
When a Line Overfits
Plain linear regression can chase noise, growing huge coefficients that fit training data yet fail on new data. That trap is called overfitting. 😬
Add a Penalty
Regularization fixes this by adding a penalty for large coefficients. The model now balances fitting the data against keeping weights small.
Ridge Shrinks Weights
Ridge penalizes the squared size of the coefficients. It pulls every weight toward zero but rarely makes any exactly zero.
from sklearn.linear_model import Ridge
model = Ridge(alpha=1.0).fit(X, y)Lasso Can Zero Them Out
Lasso penalizes the absolute size instead. This can drive weak coefficients all the way to zero, dropping those features entirely.
from sklearn.linear_model import Lasso
model = Lasso(alpha=0.1).fit(X, y)Lasso Selects Features
Because Lasso zeroes weak weights, it doubles as feature selection. The surviving non-zero coefficients are the ones it found useful.
Alpha Sets the Strength
The alpha knob controls how hard the penalty pushes. Bigger alpha means stronger shrinkage and a simpler, smoother model.
Too Much Alpha Underfits
Crank alpha too high and coefficients shrink so far the model ignores real signal. That opposite mistake is called underfitting.
Scale Before You Penalize
Penalties compare coefficients directly, so features must share a scale first. Standardize your data or the penalty hits big-unit features unfairly.
from sklearn.preprocessing import StandardScalerRidge or Lasso?
Use Ridge when most features help a little, and Lasso when you suspect many are useless and want a sparse, lean model.
ElasticNet Blends Both
Can't decide? ElasticNet mixes the Ridge and Lasso penalties, giving you shrinkage and some feature dropping at the same time.
from sklearn.linear_model import ElasticNetTune With Cross-Validation
Never guess alpha by hand. Try a range and pick the value that scores best on held-out folds with cross-validation.
from sklearn.linear_model import RidgeCVQuick Check
One key difference sets these two apart in practice.
Recap
Regularization tames overfitting: Ridge shrinks weights, Lasso can zero them, and alpha sets the strength. Scale first, then tune. 🎯
常见问题解答
「岭回归和套索正则化」课时是免费的吗？
是的 — 「岭回归和套索正则化」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。
「岭回归和套索正则化」这节课中我会学到什么？
用惩罚项抑制过拟合 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 2 节课，共 4 节。
「岭回归和套索正则化」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。