训练线性回归模型
完整的端到端示例
训练线性回归模型 是 CoddyKit 上的免费 Data Science Academy 课时。 这是第 3 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。
A Line Through Data
Linear regression fits a straight-line relationship between your features and a numeric target. It is the classic first model for a reason. 📈
Import the Model
The estimator lives in the linear_model module. You import it once and reuse it for any regression task you meet.
from sklearn.linear_model import LinearRegressionCreate an Instance
Build a fresh model by calling the class. This blank estimator holds default settings and has not seen any data yet.
model = LinearRegression()Fit on Training Data
Now teach it with your features and target. During fit, the model finds the best line through your training points.
model.fit(X_train, y_train)Read the Slope
Each feature gets a coefficient stored in coef_. It tells you how much the prediction moves when that feature rises by one.
model.coef_Read the Intercept
The intercept is the prediction when every feature is zero. It anchors the line and is stored in intercept_ after fitting.
model.intercept_Make Predictions
Hand new feature rows to predict and you get numeric estimates back, one per row, computed straight from the fitted line.
y_pred = model.predict(X_test)The Equation Behind It
Under the hood, each prediction is just features times coefficients plus the intercept. Simple math, surprisingly powerful results.
One Feature or Many
The same call handles a single feature or dozens. With many inputs it fits a hyperplane, but your code stays exactly the same.
Linear Means a Straight Fit
Linear regression assumes a roughly straight relationship. If the pattern curves sharply, this model will underfit and miss it.
The Full Workflow
Import, create, fit, predict: four steps and you have a working regressor. This same rhythm repeats for every model you learn next.
Quick Check
Which attribute holds the per-feature weights after fitting?
Recap
You imported, created, fit, and predicted with a linear regression. Its coef_ and intercept_ even reveal what it learned. 🚀
常见问题解答
「训练线性回归模型」课时是免费的吗？
是的 — 「训练线性回归模型」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。
「训练线性回归模型」这节课中我会学到什么？
完整的端到端示例 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 3 节课，共 4 节。
「训练线性回归模型」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。