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回归指标：MAE、MSE、R2

衡量数值预测误差

回归指标：MAE、MSE、R2 是 CoddyKit 上的免费 Data Science Academy 课时。 这是第 1 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。

Why Measure Error

When a model predicts numbers, you need one honest score for how far off it is. That score is an error metric, your truth-teller. 📏

The Residual Idea

Start with the residual: the gap between the true value and the predicted one. Every regression metric is just a way to summarize these gaps.

residual = y_true - y_pred

Mean Absolute Error

MAE takes the size of each error, ignores the sign, and averages them. It tells you the typical miss in the same units as your target.

from sklearn.metrics import mean_absolute_error
mean_absolute_error(y_true, y_pred)

MAE Is Easy to Explain

Because MAE stays in real units, you can say the model is off by about 5 dollars on average. Stakeholders love that plain reading.

Mean Squared Error

MSE squares each error before averaging. Squaring punishes big misses much harder, so a few large errors dominate the score.

from sklearn.metrics import mean_squared_error
mean_squared_error(y_true, y_pred)

Squaring Changes the Units

One catch: MSE is in squared units, so dollars become squared dollars. The number is hard to interpret on its own.

Root Mean Squared Error

Take the square root of MSE to get RMSE, back in normal units. It keeps the heavy penalty on big errors but reads cleanly.

rmse = mean_squared_error(y_true, y_pred) ** 0.5

MAE vs RMSE

Choose MAE when every error matters equally, and RMSE when large mistakes are especially costly and you want them penalized more.

The R-Squared Score

R2 asks a different question: what fraction of the variation in the target your model explains. Higher means a better fit.

from sklearn.metrics import r2_score
r2_score(y_true, y_pred)

Reading R-Squared

An R2 of 1.0 is perfect, 0.0 is no better than guessing the mean. It can even go negative when a model fits worse than that.

Use Them Together

No single number tells the whole story. Pair an error metric like RMSE with R2 to see both the typical miss and the overall fit.

Quick Check

Let's pin down what each regression metric really measures.

Recap

Use MAE for plain average error, RMSE to punish big misses, and R2 for fraction of variance explained. Read them together. 🎯

常见问题解答

「回归指标：MAE、MSE、R2」课时是免费的吗？

是的 — 「回归指标：MAE、MSE、R2」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。

「回归指标：MAE、MSE、R2」这节课中我会学到什么？

衡量数值预测误差 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 1 节课，共 4 节。

「回归指标：MAE、MSE、R2」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

  1. 回归指标：MAE、MSE、R2
  2. 解读混淆矩阵
  3. 精确率、召回率和 F1
  4. ROC、AUC 和阈值
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