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add Ensemble methods
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fuqiuai committed Dec 11, 2017
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| 1.3 [核岭回归](http://scikit-learn.org/stable/modules/kernel_ridge.html) | 简称KRR | 非线性回归 | <small>[sklearn.kernel_ridge.KernelRidge](http://scikit-learn.org/stable/modules/generated/sklearn.kernel_ridge.KernelRidge.html#sklearn.kernel_ridge.KernelRidge)</small> | 将核技巧应用到岭回归(1.1.2)中 |
| 1.4 [支持向量机](http://scikit-learn.org/stable/modules/svm.html) | 1.4.1 SVC,NuSVC,LinearSVC | 多类分类 | <small>[sklearn.svm.SVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC)<br>[sklearn.svm.NuSVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVC)<br>[sklearn.svm.LinearSVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC)</small>| SVC可用于非线性分类，可指定核函数；<br>NuSVC与SVC唯一的不同是可控制支持向量的个数;<br>LinearSVC用于线性分类|
| | 1.4.2 SVR,NuSVR,LinearSVR | 回归 | <small>[sklearn.svm.SVR](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR)<br>[sklearn.svm.NuSVR](http://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html#sklearn.svm.NuSVR)<br>[sklearn.svm.LinearSVR](http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVR)</small>| 同上，将"分类"变成"回归"即可 |
| | 1.4.3 OneClassSVM | 异常值检测 | <small>[sklearn.svm.OneClassSVM](http://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html#sklearn.svm.OneClassSVM)</small>| 非监督 |
| 1.5 [SGD](http://scikit-learn.org/stable/modules/sgd.html) | 同1.1.12 | | | |
| 1.6 [最近邻](http://scikit-learn.org/stable/modules/neighbors.html) | 1.6.1 Unsupervised Nearest Neighbors | 聚类 | [sklearn.neighbors.NearestNeighbors](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors) | |
| | 1.6.2 Nearest Neighbors Classification | | | |
| | 1.4.3 OneClassSVM | 异常值检测 | <small>[sklearn.svm.OneClassSVM](http://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html#sklearn.svm.OneClassSVM)</small>| 无监督 |
| 1.5 [随机梯度下降](http://scikit-learn.org/stable/modules/sgd.html) | 同1.1.12 | | | |
| 1.6 [最近邻](http://scikit-learn.org/stable/modules/neighbors.html) | 1.6.1 Unsupervised Nearest Neighbors | 寻找K近邻 | [sklearn.neighbors.NearestNeighbors](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors) | 无监督 |
| | 1.6.2 Nearest Neighbors Classification | 多类分类 | [sklearn.neighbors.KNeighborsClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier)<br>[sklearn.neighbors.RadiusNeighborsClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.html#sklearn.neighbors.RadiusNeighborsClassifier) | (1)不太适用于高维数据<br>(2)两种实现只是距离度量不一样，后者更适合非均匀的采样 |
| | 1.6.3 Nearest Neighbors Regression| 回归 | [sklearn.neighbors.KNeighborsRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor)<br>[sklearn.neighbors.RadiusNeighborsRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.html#sklearn.neighbors.RadiusNeighborsRegressor) | 同上 |
| | 1.6.5 Nearest Centroid Classifier | 多类分类 | [sklearn.neighbors.NearestCentroid](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestCentroid.html#sklearn.neighbors.NearestCentroid) | 每个类对应一个质心，测试样本被分类到距离最近的质心所在的类别 |
| 1.7 [高斯过程(GP/GPML)](http://scikit-learn.org/stable/modules/gaussian_process.html) | 1.7.1 GPR | 回归 | [sklearn.gaussian_process.<br>GaussianProcessRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor) | 与KRR一样使用了核技巧 |
| | 1.7.3 GPC | 多类分类 | [sklearn.gaussian_process.<br>GaussianProcessClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html#sklearn.gaussian_process.GaussianProcessClassifier) | |
| 1.8 [交叉分解](http://scikit-learn.org/stable/modules/cross_decomposition.html) | 实现算法：CCA和PLS | | | 用来计算两个多元数据集的线性关系，当预测数据比观测数据有更多的变量时，用PLS更好 |
| 1.9 [朴素贝叶斯](http://scikit-learn.org/stable/modules/naive_bayes.html) | 1.9.1 高斯朴素贝叶斯 | 多类分类 | [sklearn.naive_bayes.GaussianNB](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB) | 处理特征是连续型变量的情况 |
| | 1.9.2 多项式朴素贝叶斯 | 多类分类 | [sklearn.naive_bayes.MultinomialNB](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html#sklearn.naive_bayes.MultinomialNB) | 最常见，要求特征是离散数据 |
| | 1.9.3 伯努利朴素贝叶斯 | 多类分类 | [sklearn.naive_bayes.BernoulliNB](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB) | 要求特征是离散的，且为布尔类型，即true和false，或者1和0 |
| 1.10 [决策树](http://scikit-learn.org/stable/modules/tree.html) | 1.10.1 Classification | 多类分类 | [sklearn.tree.DecisionTreeClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier) | |
| | 1.10.2 Regression | 回归 | [sklearn.tree.DecisionTreeRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor) | |
| 1.11 [集成方法](http://scikit-learn.org/stable/modules/ensemble.html) | 1.11.1 Bagging | 分类/回归 | [sklearn.ensemble.BaggingClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html#sklearn.ensemble.BaggingClassifier)<br>[sklearn.ensemble.BaggingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html#sklearn.ensemble.BaggingRegressor) | 可以指定基学习器，默认为决策树 |
| | 1.11.2 Forests of randomized trees | 分类/回归 | RandomForest(RF,随机森林):<br>[sklearn.ensemble.RandomForestClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier)<br>[sklearn.ensemble.RandomForestRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor)<br>ExtraTrees(RF改进):<br>[sklearn.ensemble.ExtraTreesClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier)<br>[sklearn.ensemble.ExtraTreesRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html#sklearn.ensemble.ExtraTreesRegressor) | 基学习器为决策树 |
| | 1.11.3 AdaBoost | 分类/回归 | [sklearn.ensemble.AdaBoostClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier)<br>[sklearn.ensemble.AdaBoostRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostRegressor.html#sklearn.ensemble.AdaBoostRegressor) | 可以指定基学习器，默认为决策树 |
| | 1.11.4 Gradient Tree Boosting(GBRT)| 分类/回归 | [sklearn.ensemble.GradientBoostingClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier)<br>[sklearn.ensemble.GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor) | 基学习器为决策树 |
| | 1.11.5 Voting Classifier | 分类 | [sklearn.ensemble.VotingClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier) | |
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