特征 X 和目标 y
构造库所需的输入
特征 X 和目标 y 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。
Inputs and Outputs
Supervised models learn a mapping from inputs to an output. The inputs are your features, and the output is what you want to predict. 🎯
Meet X and y
By convention, features go in a capital X and the target goes in a lowercase y. This naming shows up in nearly every example you read.
X Is Two-Dimensional
Capital X is a 2D table: one row per sample, one column per feature. scikit-learn always expects this rows-by-columns shape.
X.shape # (n_samples, n_features)y Is One-Dimensional
The target y is usually a single column: one value per sample. Its length must match the number of rows in X exactly.
y.shape # (n_samples,)Split a DataFrame
From a pandas table, you carve out X and y by selecting columns. The target column becomes y; everything else becomes X.
X = df.drop(columns=['price'])
y = df['price']Features Should Be Numeric
Most estimators expect numeric features. Text and category columns need encoding into numbers before they can enter X.
Keep the Target Out of X
Never leave the answer inside your features. A target hiding in X is leakage, and it makes a model look perfect but useless.
Rows Must Line Up
Row one of X must correspond to value one of y. This alignment is how the model connects each sample to its correct answer.
Regression vs Classification
If y holds continuous numbers it is regression; if y holds categories it is classification. The target's type decides the model family.
Feed Both Into fit
Once X and y are ready, you hand both to fit together. The model reads features and answers side by side to learn.
model.fit(X, y)predict Takes X Only
When predicting, you pass new features with the same columns as training X. You never pass y, since that is what you want back.
model.predict(X_new)Quick Check
Make sure you have the shapes of X and y straight.
Recap
Split your data into X, a 2D feature table, and y, the 1D target. Keep them aligned and leak-free, and fit is ready to learn. ✅
常见问题解答
「特征 X 和目标 y」课时是免费的吗？
是的 — 「特征 X 和目标 y」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。
「特征 X 和目标 y」这节课中我会学到什么？
构造库所需的输入 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。
「特征 X 和目标 y」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
- fit 和 predict 契约
- 特征 X 和目标 y
- 训练线性回归模型
- 为您的第一个模型评分