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监督学习

分类算法

 

$1.KNN最近邻算法：$

Step 1 算距离：计算训练样本和测试样本中每个样本点的距离（欧氏距离、马氏距离、曼哈顿距离）  Step 2 排序：对上面所有的距离值进行排序；   Step 3 找邻居：选前K个最小距离的样本   Step 4 做分类：根据这K个样本标签进行投票，得到最后的分类类别

$优点：$

1.简单、有效

2.重新训练的代价低，只需要将新样本加入训练计算而不需要重新训练

3.计算时间和空间与训练集的规模成线性关系

4.对于类域的交叉或重叠较多的待分样本集，KNN更为有效

5.适用于样本容量较大的类域的自动分类，样本较小的类域容易产生误分

6.对数据没有假设，准确度高，对离群值不敏感

7.可用于非线性分类

缺点：

KNN属于懒散学习，比积极的算法快

类别评分不是规格化的

输出的可解释性不强

分类时，如果样本不平衡，会导致计算量较大，时间复杂度为O(N)，需要保存全部数据集，内存消耗大

需要给定K值

每一次分类都需要重新计算

应用领域： 文本分类、模式识别、聚类分析、多分类

$2.朴素贝叶斯分类：$

Step 1：计算每个类别的概率 Step 2：计算每个属性的条件概率 Step 3：计算各个属性的联合概率分布 Step 4：由贝叶斯公式计算其属于不同类别的概率，确定其分类；对于连续值，可以假设其分布函数从而得到条件概率

朴素贝叶斯网络的训练阶段就是确定在整个样本集下每个类别的概率、以及每个特征每个属性的条件概率，在预测阶段只需要调用训练中的结果

优点： 理论基础牢固、有稳定的分类效率 对小规模数据集表现好，能处理多分类任务 适合增量式训练，可以批训练 对缺失数据不敏感，算法简单 结果的可解释性好

缺点： 朴素贝叶斯的特征独立性假设不一定能满足，样本属性间 需要知道先验概率，且先验概率取决于假设，但假设可能有很多种 由于是通过先验和数据决定后验的概率来决定分类，决策具有一定的错误率 对输入数据的表达形式很敏感

应用领域： 文本分类、欺诈检测

参考链接：[[https://blog.csdn.net/qiu_zhi_liao/article/details/90671932]]

$3.决策树：$

优点： 易于理解和解释，可以可视化分析，容易提取出规则 在相对较短的时间内能够对大型数据源做出可行且效果良好的结果 可以同时处理标称型和数值型数据 比较适合处理有缺失属性的样本 能够处理不相关的特征 可以处理参数或非参数化特征间的交互关系 可以处理非线性特征

缺点： 对缺失数据处理比较困难 容易出现过拟合，容易往树的深度加深的方向学习 忽略了数据集中的相互关联 不同的分裂选择算法会偏向不同的结果 不支持在线学习，在新样本到来后决策树需要重建

$4.逻辑回归：$

优点： 实现简单 分类时计算量小，速度快，储存资源低 便利的观测样本概率分数 可以通过L2正则化解决多重共线性问题 扩展SOFTMAX可以进行多分类

缺点： 特征空间很大时，逻辑回归性能下降 容易欠拟合，分类精度不高 不能很好地处理大量多类特征或变量 只能处理两分类问题，且必须先线性可分

$5.SVM支持向量机：$

高准确率，在原特征空间线性不可分的情况下，引入合适的核函数也能准确分类

优点： 可以解决高维问题，大型特征空间 能够处理非线性特征的相互作用 解决小样本下的机器学习问题 无需依赖整个数据 泛化能力比较强 无局部极小值问题

缺点： 当观测样本很多时，效率降低 对非线性问题没有通用解决方案，很难找到合适的核函数 对缺失数据敏感

应用： 文本分类、图像识别、二分类

$6.Adaboost：$

优点： 具有高精度 简单，不用做特征筛选 结果具有较好的可解释性 不容易overfitting 充分考虑了每个分类器的权重

缺点： 对outlier比较敏感 AdaBoost迭代次数、弱分类数目不好设定（可以使用交叉验证） 数据不平衡容易导致分类精度下降 训练耗时，每次重新选择当前分类器最好切分点

$7.GBDT：$

优点： 预测精度高 适合低维数据 适合处理非线性数据 可以灵活处理各种类型数据，包括连续值和离散值 使用健壮的损失函数可以增强对异常值的鲁棒性 在较短的调参时间下，预测准确度较高

缺点： 弱学习器间存在依赖关系，难以并行训练数据 数据维度较高的情况下算法复杂度

$8.XGBoost：$

优点： 相较于GBDT来说，XGBoost支持线性分类器，等价于L1和L2正则化的逻辑回归、线性回归 XGBoost在代价函数中加入正则化项，用于控制模型复杂度，降低模型方差，学习出来的模型更加简单，防止过拟合 shrinkage，缩减，相当于学习速率衰减 列抽样，可并行 对缺失值的处理，XGBoost可以自动学习出缺失值的分裂方向 XGBoost支持并行，并行是在特征粒度上，XGBoost是在特征值上进行排序，保存为block结构，在后面的重复迭代使用该结构，减小计算量。并且，计算每个特征的增益时，最终选择增益最大的特征去做分裂，各个特征的增益计算可以进行多线程计算

缺点：

$9.LightGBM：$

$10.RF随机森林：$

优点： 采用集成算法，精度比单个决策树算法好 在测试集上表现良好，由于两个随机性：样本随机、特征随机，能够降低过拟合风险，提高抗噪声能力 由于树的组合，使得随机森林可以处理非线性数据，本身属于非线性分类、拟合模型 能够处理很高维度的数据，并且不用做特征选择，对数据的适应能力强 既能处理离散型数据，也能处理连续型数据，数据无需规范化 训练速度快，可以运用在大规模数据集上 可以处理缺省值（将其归于一类） 对于集外数据，可以在模型生成过程中取得真实误差的无偏估计，且不损失训练数据 在训练数据中，能够检测特征间的互相影响，且可以得到特征的重要性 每棵树可以独立同时生成，容易并行化 实现简单、精度高、抗过拟合能力强

缺点： 在噪声较大的分类、回归问题上容易过拟合

$11.线性回归：$

优点： 可解释性好 模型简单

缺点： 不能处理非线性关系

$非监督学习$

强化学习

聚类算法

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.edge-pattern-dashed{stroke-dasharray:3}#mermaid-svg-MCB9pTXDoyl7up57 .edge-pattern-dotted{stroke-dasharray:2}#mermaid-svg-MCB9pTXDoyl7up57 .marker{fill:#333}#mermaid-svg-MCB9pTXDoyl7up57 .marker.cross{stroke:#333} :root { --mermaid-font-family: "trebuchet ms", verdana, arial;} #mermaid-svg-MCB9pTXDoyl7up57 { color: rgba(0, 0, 0, 0.75); font: ; } 聚类算法 原型聚类 层次聚类 密度聚类 谱聚类 K均值聚类 学习向量化 高斯混合聚类 AGNES BIRCH DBSCAN Mean-Shift

原型聚类

EM算法

只要有一些训练数据，再定义一个最大化函数，采用EM算法，利用计算机经过若干次迭代，就可以得到所需的模型。EM算法是自收敛的分类算法，既不需要事先设定类别也不需要数据见的两两比较合并等操作。缺点是当所要优化的函数不是凸函数时，EM算法容易给出局部最佳解，而不是最优解。

K均值聚类：K-Means

学习向量化：LVQ

高斯混合聚类

密度聚类

DBSCAN

Mean-Shift

层次聚类

AGNES

BIRCH

谱聚类

降维算法

线性降维

PCA LDA

非线性降维

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生成式模型和判别式模型

判别式模型寻找的是不同类别之间的最优分类面，反映的是异类数据之间的差异，估计的是条件概率分布，直接预测的准确度更高 生成式模型对后验概率建模。从统计的角度表示数据的分布情况，能够反映同类数据之间的相似度，估计的是联合概率分布

判别式模型直接计算条件概率，生成式模型先计算联合概率分布，按照联合概率分布再计算条件概率，生成式模型可以体现更多数据本身的分布信息，更具有普适性

![[Pasted image.png]] ![[Pasted image 1.png]] 左图判别式模型是在寻找一个决策边界，通过该边界来将样本划分到对应类别，生成式模型学习了每个类别的边界，包含了更多信息，可以用来生成样本 对条件概率建模，学习不同类别之间的最优边界。 捕捉不同类别特征的差异信息，不学习本身分布信息，无法反应数据本身特性。 $判别式模型特点：$ 1.学习成本较低，需要的计算资源较少。 2.需要的样本数可以较少，少样本也能很好学习。 3.预测时拥有较好性能。 4.无法转换成生成式 $生成式模型特点：$ 生成式模型学习的是联合概率密度分布 [公式] ，可以从统计的角度表示分布的情况，能够反映同类数据本身的相似度，它不关心到底划分不同类的边界在哪里。生成式模型的学习收敛速度更快，当样本容量增加时，学习到的模型可以更快的收敛到真实模型，当存在隐变量时，依旧可以用生成式模型，此时判别式方法就不行了 1.对联合概率建模，学习所有分类数据的分布。 2.学习到的数据本身信息更多，能反应数据本身特性。 3.学习成本较高，需要更多的计算资源。 4.需要的样本数更多，样本较少时学习效果较差。 5.推断时性能较差。 6.一定条件下能转换成判别式

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.edge-pattern-dashed{stroke-dasharray:3}#mermaid-svg-uweH3gAkgtWZTTzk .edge-pattern-dotted{stroke-dasharray:2}#mermaid-svg-uweH3gAkgtWZTTzk .marker{fill:#333}#mermaid-svg-uweH3gAkgtWZTTzk .marker.cross{stroke:#333} :root { --mermaid-font-family: "trebuchet ms", verdana, arial;} #mermaid-svg-uweH3gAkgtWZTTzk { color: rgba(0, 0, 0, 0.75); font: ; } 机器学习模型 判别式模型 生成式模型 线性回归 逻辑回归 神经网络NN 支持向量机 高斯过程 条件随机场CRF 决策树 集成学习Boosting K近邻 最大熵模型 区分度训练 线性判别分析LDA 朴素贝叶斯 混合高斯模型 KNN,K近邻 隐马尔科夫模型HMM 贝叶斯网络 马尔科夫随机场 深度信念网络DBN 隐含狄利克雷分布LDA 生成对抗网络 变分自动编码器 自回归模型AR

常见机器学习模型总结

类别方法适用情况损失函数学习策略优化算法监督学习线性模型二分类MSE极小化误分类点到超平面距离最小二乘/梯度下降监督学习逻辑回归二分类对数损失函数极大似然估计梯度下降决策树多分类对数似然损失极大似然估计特征选择、生成、剪枝支持向量机二分类合页损失函数支持向量离超平面软间隔最大化序列最小最优化算法SMO朴素贝叶斯多分类对数损失函数极大似然估计、极大后验概率估计EM算法