k 近邻
根据最接近的示例进行分类
k 近邻 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。
Judged by Your Neighbors
k-Nearest Neighbors classifies a point by looking at the examples closest to it. Birds of a feather end up in the same class. 🐦
No Real Training
kNN is a lazy learner: it just memorizes the data. The real work happens later, when a new point asks for a label.
Measure the Distance
To find who is near, the model computes a distance to every training point, most often plain straight-line distance.
Take a Vote
The closest k neighbors each cast a vote. Whichever class wins the majority becomes the prediction for the new point.
k Is the Key Choice
The letter k is how many neighbors you consult. It is the one knob that most shapes how kNN behaves.
Small k, Jumpy Borders
A tiny k, like 1, follows every wiggle in the data and reacts to noise, giving jagged, overfit boundaries.
Large k, Smoother Calls
A big k averages over many points for smoother decisions, but too large and it blurs the real differences between classes.
Scaling Is Not Optional
Distance is unfair if one feature spans thousands and another spans tenths. Always scale features so each counts equally.
Build the Classifier
Create it like any estimator and pick your n_neighbors right away. Here we consult the five closest points.
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=5)Fit, Then Predict
The familiar contract still applies: fit stores the data, predict finds neighbors and votes on the answer.
model.fit(X_train, y_train)
model.predict(X_test)Watch the Cost
kNN is simple but can be slow at prediction time, since every guess compares the new point to all stored examples.
Quick Check
Let's pin down the parameter that defines kNN.
Recap
kNN predicts by letting the closest k neighbors vote. Choose k with care and scale your features, and it works well. 🎯
常见问题解答
「k 近邻」课时是免费的吗？
是的 — 「k 近邻」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。
「k 近邻」这节课中我会学到什么？
根据最接近的示例进行分类 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。
「k 近邻」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。