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用于回归的 MSE 与 MAE

惩罚预测值与目标值之间的距离

用于回归的 MSE 与 MAE 是 CoddyKit 上的免费 Deep Learning 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 Deep Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Deep Learning 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。

Regression Needs Its Own Loss

When your model predicts a number like a price or temperature, you need a regression loss that measures how far each guess lands from the true value. 📏

Meet Mean Squared Error

MSE squares every prediction error, then averages them. Squaring keeps all errors positive and punishes big misses much harder than small ones.

MSE in One Line

PyTorch gives you nn.MSELoss, so you never write the formula by hand. You pass predictions and targets and get a single scalar back.

import torch.nn as nn
loss_fn = nn.MSELoss()
loss = loss_fn(pred, target)

Why Squaring Matters

Because MSE squares errors, an error of 4 counts 16x more than an error of 1. That makes MSE very sensitive to large mistakes and outliers.

Meet Mean Absolute Error

MAE averages the absolute size of each error instead of squaring it. Every mistake counts in proportion to how big it actually is.

MAE in PyTorch

Use nn.L1Loss for MAE. The name comes from the L1 norm, which simply sums absolute differences before averaging.

import torch.nn as nn
loss_fn = nn.L1Loss()
loss = loss_fn(pred, target)

MAE Shrugs Off Outliers

Since MAE never squares anything, one wild outlier does not blow up the loss. That makes it the calmer, more robust choice for messy data.

MSE vs MAE: The Tradeoff

Pick MSE when big errors are truly worse and your data is clean. Pick MAE when outliers are noise you do not want to chase.

What the Number Means

A regression loss is in your target's units (squared for MSE). Lower is better, but compare it across models, not as an absolute score.

Huber: The Best of Both

Want robustness and smooth gradients? Huber loss acts like MSE for small errors and like MAE for large ones, blending both behaviors.

import torch.nn as nn
loss_fn = nn.SmoothL1Loss()
loss = loss_fn(pred, target)

Match Loss to Output Shape

For regression your final layer outputs raw numbers with no activation. The loss compares those values straight to the targets.

Quick Check

One outlier is dominating your training. Which loss is most robust to it?

Recap: Distance, Measured

You now read regression error two ways: MSE punishes big misses by squaring, while MAE stays steady against outliers. Match the loss to your data and you are set. 🎯

常见问题解答

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是的 — 「用于回归的 MSE 与 MAE」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 Deep Learning Academy 课程的其余内容，请升级到 CoddyKit PRO。 Deep Learning Academy 课程共包含 4 节课。

「用于回归的 MSE 与 MAE」这节课中我会学到什么？

惩罚预测值与目标值之间的距离你通过在浏览器中直接运行的动手代码来练习 Deep Learning 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。

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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。

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