> For the complete documentation index, see [llms.txt](https://windmising.gitbook.io/liu-yu-bo-play-with-machine-learning/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://windmising.gitbook.io/liu-yu-bo-play-with-machine-learning/src/summary.md).

# Summary

* [Introduction](/liu-yu-bo-play-with-machine-learning/src/introduction.md)
* [第四章：K近邻算法](/liu-yu-bo-play-with-machine-learning/src/chapter4/4-0.md)
  * [4-1 K近邻算法基础](/liu-yu-bo-play-with-machine-learning/src/chapter4-1/4-1.md)
  * [4-2 scikit-learn中的机器学习算法的封装](/liu-yu-bo-play-with-machine-learning/src/chapter4-2/4-2.md)
  * [4-3 训练数据集，测试数据集](/liu-yu-bo-play-with-machine-learning/src/chapter4-3/4-3.md)
  * [4-4 分类准确度](/liu-yu-bo-play-with-machine-learning/src/chapter4-4/4-4.md)
  * [4-5 超参数](/liu-yu-bo-play-with-machine-learning/src/chapter4-5/4-5.md)
  * [4-6 网格搜索](/liu-yu-bo-play-with-machine-learning/src/chapter4-6/4-6.md)
  * [4-7 数据归一化 Feature Scaling](/liu-yu-bo-play-with-machine-learning/src/chapter4-7/4-7.md)
  * [4-8 scikit-learn中的Scaler](/liu-yu-bo-play-with-machine-learning/src/chapter4-8/4-8.md)
  * [4-9 更多有关K近邻算法的思考](/liu-yu-bo-play-with-machine-learning/src/chapter4-9/4-9.md)
* [第五章：线性回归法](/liu-yu-bo-play-with-machine-learning/src/chapter5/5-0.md)
  * [5-1 简单线性回归](/liu-yu-bo-play-with-machine-learning/src/chapter5-1/5-1.md)
  * [5-2 最小二乘法](/liu-yu-bo-play-with-machine-learning/src/chapter5-3/5-2.md)
  * [5-3 简单线性回归的实现](/liu-yu-bo-play-with-machine-learning/src/chapter5-4/5-3.md)
  * [5-4 参数计算向量化](/liu-yu-bo-play-with-machine-learning/src/chapter5-5/5-4.md)
  * [5-5 衡量线性回归算法的指标](/liu-yu-bo-play-with-machine-learning/src/chapter5-6/5-5.md)
  * [5-6 最好的衡量线性回归法的指标 R Squared](/liu-yu-bo-play-with-machine-learning/src/chapter5-7/5-6.md)
  * [5-7 简单线性回归和正规方程解](/liu-yu-bo-play-with-machine-learning/src/chapter5-8/5-7.md)
  * [5-8 实现多元线性回归](/liu-yu-bo-play-with-machine-learning/src/chapter5-9/5-8.md)
  * [5-9 scikit-learn中的回归算法](/liu-yu-bo-play-with-machine-learning/src/chapter5-10/5-9.md)
  * [5-10 线性回归的可解释性和更多思考](/liu-yu-bo-play-with-machine-learning/src/chapter5-2/5-10.md)
* [第六章：梯度下降法](/liu-yu-bo-play-with-machine-learning/src/chapter6/6-1.md)
  * [6-1 什么是梯度下降法](/liu-yu-bo-play-with-machine-learning/src/chapter6/6-1.md)
  * [6-2 模拟实现梯度下降法](/liu-yu-bo-play-with-machine-learning/src/chapter6-1/6-2.md)
  * [6-3 多元线性回归中的梯度下降法](/liu-yu-bo-play-with-machine-learning/src/chapter6-2/6-3.md)
  * [6-4 在线性回归模型中使用梯度下降法](/liu-yu-bo-play-with-machine-learning/src/chapter6-3/6-4.md)
  * [6-5 梯度下降的向量化](/liu-yu-bo-play-with-machine-learning/src/chapter6-4/6-5.md)
  * [6-6 随机梯度下降](/liu-yu-bo-play-with-machine-learning/src/chapter6-5/6-6.md)
  * [6-7 代码实现随机梯度下降](/liu-yu-bo-play-with-machine-learning/src/chapter6-6/6-7.md)
  * [6-8 调试梯度下降法](/liu-yu-bo-play-with-machine-learning/src/chapter6-7/6-8.md)
  * [6-9 有关梯度下降法的更多深入讨论](/liu-yu-bo-play-with-machine-learning/src/chapter6-8/6-9.md)
* [第七章：PCA与梯度上升法](/liu-yu-bo-play-with-machine-learning/src/chapter7/7-0.md)
  * [7-1 什么是PCA](/liu-yu-bo-play-with-machine-learning/src/chapter7-1/7-1.md)
  * [7-2 使用梯度上升法求解主成分分析问题](/liu-yu-bo-play-with-machine-learning/src/chapter7-2/7-2.md)
  * [7-3 代码实现主成分分析问题](/liu-yu-bo-play-with-machine-learning/src/chapter7-3/7-3.md)
  * [7-4 求数据的前N个主成分](/liu-yu-bo-play-with-machine-learning/src/chapter7-4/7-4.md)
  * [7-5 高维数据向低维数据映射](/liu-yu-bo-play-with-machine-learning/src/chapter7-5/7-5.md)
  * [7-6 scikit learn中的PCA](/liu-yu-bo-play-with-machine-learning/src/chapter7-6/7-6.md)
  * [7-7 MNIST数据集](/liu-yu-bo-play-with-machine-learning/src/chapter7-7/7-7.md)
  * [7-8 使用PCA降噪](/liu-yu-bo-play-with-machine-learning/src/chapter7-8/7-8.md)
  * [7-9 人脸识别和特征脸(未完成)](/liu-yu-bo-play-with-machine-learning/src/chapter7-9/7-9.md)
* [第八章：多项式回归与模型泛化](/liu-yu-bo-play-with-machine-learning/src/chapter8/8-1.md)
  * [8-1 什么是多项式回归](/liu-yu-bo-play-with-machine-learning/src/chapter8/8-1.md)
  * [8-2 scikit-learn中的多项式回归和pipeline](/liu-yu-bo-play-with-machine-learning/src/chapter8-2/8-2.md)
  * [8-3 过拟合和欠拟合](/liu-yu-bo-play-with-machine-learning/src/chapter8-3/8-3.md)
  * [8-4 为什么要训练数据集和测试数据集](/liu-yu-bo-play-with-machine-learning/src/chapter8-4/8-4.md)
  * [8-5 学习曲线](/liu-yu-bo-play-with-machine-learning/src/chapter8-5/8-5.md)
  * [8-6 验证数据集与交叉验证](/liu-yu-bo-play-with-machine-learning/src/chapter8-6/8-6.md)
  * [8-7 偏差方差权衡 Bias Variance Trade off](/liu-yu-bo-play-with-machine-learning/src/chapter8-7/8-7.md)
  * [8-8 模型正则化 Regularization](/liu-yu-bo-play-with-machine-learning/src/chapter8-8/8-8.md)
  * [8-9 LASSO Regularization](/liu-yu-bo-play-with-machine-learning/src/chapter8-9/8-9.md)
  * [8-10 L1,L2和弹性网络](/liu-yu-bo-play-with-machine-learning/src/chapter8-1/8-10.md)
* [第九章：逻辑回归](/liu-yu-bo-play-with-machine-learning/src/chapter9/9-1.md)
  * [9-1 逻辑回归 Logistic Regression](/liu-yu-bo-play-with-machine-learning/src/chapter9/9-1.md)
  * [9-2 逻辑回归的损失函数](/liu-yu-bo-play-with-machine-learning/src/chapter9-1/9-2.md)
  * [9-3 逻辑回归算法损失函数的梯度](/liu-yu-bo-play-with-machine-learning/src/chapter9-2/9-3.md)
  * [9-4 实现逻辑回归算法](/liu-yu-bo-play-with-machine-learning/src/chapter9-3/9-4.md)
  * [9-5 决策边界](/liu-yu-bo-play-with-machine-learning/src/chapter9-4/9-5.md)
  * [9-6 在逻辑回归中使用多项式特征](/liu-yu-bo-play-with-machine-learning/src/chapter9-5/9-6.md)
  * [9-7 scikit-learn中的逻辑回归](/liu-yu-bo-play-with-machine-learning/src/chapter9-6/9-7.md)
  * [9-8 OvR与OvO](/liu-yu-bo-play-with-machine-learning/src/chapter9-7/9-8.md)
* [第十章：评价分类结果](/liu-yu-bo-play-with-machine-learning/src/chapter10/10-1.md)
  * [10-1 准确度的陷阱和混淆矩阵](/liu-yu-bo-play-with-machine-learning/src/chapter10/10-1.md)
  * [10-2 精确率和召回率](/liu-yu-bo-play-with-machine-learning/src/chapter10-1/10-2.md)
  * [10-3 实现混淆矩阵、精准率、召回率](/liu-yu-bo-play-with-machine-learning/src/chapter10-2/10-3.md)
  * [10-4 F1 score](/liu-yu-bo-play-with-machine-learning/src/chapter10-3/10-4.md)
  * [10-5 Precision-Recall平衡](/liu-yu-bo-play-with-machine-learning/src/chapter10-4/10-5.md)
  * [10-6 precision-recall曲线](/liu-yu-bo-play-with-machine-learning/src/chapter10-5/10-6.md)
  * [10-7 ROC曲线](/liu-yu-bo-play-with-machine-learning/src/chapter10-6/10-7.md)
  * [10-8 多分类问题中的混淆矩阵](/liu-yu-bo-play-with-machine-learning/src/chapter10-7/10-8.md)
* [第十一章：支撑向量机 SVM](/liu-yu-bo-play-with-machine-learning/src/chapter11/11-1.md)
  * [11-1 什么是支撑向量机](/liu-yu-bo-play-with-machine-learning/src/chapter11/11-1.md)
  * [11-2 支撑向量机的推导过程](/liu-yu-bo-play-with-machine-learning/src/chapter11-1/11-2.md)
  * [11-3 Soft Margin和SVM的正则化](/liu-yu-bo-play-with-machine-learning/src/chapter11-2/11-3.md)
  * [11-4 scikit-leran中的SVM](/liu-yu-bo-play-with-machine-learning/src/chapter11-3/11-4.md)
  * [11-5 SVM中使用多项式特征](/liu-yu-bo-play-with-machine-learning/src/chapter11-4/11-5.md)
  * [11-6 什么是核函数](/liu-yu-bo-play-with-machine-learning/src/chapter11-5/11-6.md)
  * [11-7 高斯核函数](/liu-yu-bo-play-with-machine-learning/src/chapter11-6/11-7.md)
  * [11-8 scikit-learn中的高斯核函数](/liu-yu-bo-play-with-machine-learning/src/chapter11-7/11-8.md)
  * [11-9 SVM思想解决回归问题](/liu-yu-bo-play-with-machine-learning/src/chapter11-8/11-9.md)
* [第十二章：决策树](/liu-yu-bo-play-with-machine-learning/src/chapter12/12-1.md)
  * [12-1 什么是决策树](/liu-yu-bo-play-with-machine-learning/src/chapter12/12-1.md)
  * [12-2 信息熵](/liu-yu-bo-play-with-machine-learning/src/chapter12-1/12-2.md)
  * [12-3 使用信息寻找最优划分](/liu-yu-bo-play-with-machine-learning/src/chapter12-2/12-3.md)
  * [12-4 基尼系数](/liu-yu-bo-play-with-machine-learning/src/chapter12-3/12-4.md)
  * [12-5 CART和决策树中的超参数](/liu-yu-bo-play-with-machine-learning/src/chapter12-4/12-5.md)
  * [12-6 决策树解决回归问题](/liu-yu-bo-play-with-machine-learning/src/chapter12-5/12-6.md)
  * [12-7 决策树的局限性](/liu-yu-bo-play-with-machine-learning/src/chapter12-6/12-7.md)
* [第十三章：集成学习和随机森林](/liu-yu-bo-play-with-machine-learning/src/chapter13/13-1.md)
  * [13-1 什么是集成学习](/liu-yu-bo-play-with-machine-learning/src/chapter13/13-1.md)
  * [13-2 soft voting](/liu-yu-bo-play-with-machine-learning/src/chapter13-1/13-2.md)
  * [13-3 bagging和pasting](/liu-yu-bo-play-with-machine-learning/src/chapter13-2/13-3.md)
  * [13-4 更多关于bagging的讨论](/liu-yu-bo-play-with-machine-learning/src/chapter13-3/13-4.md)
  * [13-5 随机森林和extra-trees](/liu-yu-bo-play-with-machine-learning/src/chapter13-4/13-5.md)
  * [13-6 ada boosting和gradiesnt boosting](/liu-yu-bo-play-with-machine-learning/src/chapter13-5/13-6.md)
  * [13-7 Stacking](/liu-yu-bo-play-with-machine-learning/src/chapter13-6/13-7.md)