Machine learningMachine learning
线性回归 (ML)
线性回归通过最小化预测误差的平方和，来拟合一个或多个输入特征与连续数值型结果之间的一条直线关系。作为一种机器学习模型，它在标记样本上进行训练，并在保留数据上进行评估，使其成为任何回归任务最简单的监督学习基线。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7
- James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 3). Springer. ISBN: 978-1-4614-7138-7
如何引用本页
ScholarGate. (2026, June 3). Linear Regression as a Machine Learning Model. ScholarGate. https://scholargate.app/zh/machine-learning/linear-regression-ml
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 决策树机器学习↔ compare
- 梯度提升（Gradient Boosting）机器学习↔ compare
- 逻辑回归（机器学习）机器学习↔ compare
- 随机森林机器学习↔ compare
- 正则化线性回归机器学习↔ compare