The methods based on F-test estimate the degree of linear dependency between two random variables. On the other hand, mutual information methods can capture any kind of statistical dependency, but being nonparametric, they require more samples for accurate estimation. Univariate Feature Selection -- ...
Feature selection using SelectFromModel and LassoCV: 从 Boston 数据中自动选择最重要两个特征而不需要提前得知这一信息。 1.13.4.1. 基于 L1 的特征选取 Linear models 使用 L1 正则化的线性模型会得到稀疏解:他们的许多系数为 0。 当目标是降低使用另一个分类器的数据集的维度, 它们可以与feature_selection.S...
sklearn.model_selection: Model Selection sklearn.datasets: Datasets sklearn.decomposition: Matrix Decomposition sklearn.dummy: Dummy estimators sklearn.ensemble: Ensemble Methods sklearn.exceptions: Exceptions and warnings sklearn.feature_extraction: Feature Extraction sklearn.feature_selection: Feature Select...
8.sklearn.dummy: Dummy estimators 虚拟估计 9.sklearn.ensemble: Ensemble Methods 集成方法 10.sklearn.exceptions: Exceptions and warnings 异常和警告 11.sklearn.feature_extraction: Feature Extraction 特征抽取 12.sklearn.feature_selection: Feature Selection 特征选择 13。sklearn.gaussian_process: Gaussian ...
特征选择(Feature Selection): SelectKBest、SelectFromModel 过滤方法(Filter Methods):如选择相关性高的特征或基于统计测试的特征。 包装方法(Wrapper Methods):如递归特征消除(RFE)。 嵌入方法(Embedded Methods):如使用模型的系数进行特征选择。 SHai:数据集特征选择0 赞同 · 0 评论文章 特征提取(Feature Extractio...
集成学习方法(Ensemble methods) 多类和多标签算法 特征选择(Feature selection) 半监督学习 等式回归 概率校准(Probability calibration) 神经网络模型(有监督) 4. sklearn 无监督学习 Table of contents 1.6.1. 无监督最近邻(Unsupervised Nearest Neighbors) 1.6.1.1. 寻找最近的邻居 1.6.1.2. KDTre...
sklearn.model_selection: Model Selection sklearn.datasets: Datasets sklearn.decomposition: Matrix Decomposition sklearn.dummy: Dummy estimators sklearn.ensemble: Ensemble Methods sklearn.exceptions: Exceptions and warnings sklearn.feature_extraction: Feature Extraction ...
sklearn.ensemble: Ensemble Methods 集成方法 sklearn.exceptions: Exceptions and warnings 例外和警告 sklearn.feature_extraction: Feature Extraction 特征提取 sklearn.feature_selection: Feature Selection 特征选择 sklearn.gaussian_process: Gaussian Processes 高斯过程 ...
集成学习方法(Ensemble methods) 多类和多标签算法 特征选择(Feature selection) 半监督学习 等式回归 概率校准(Probability calibration) 神经网络模型(有监督) 4. sklearn 无监督学习 Table of contents 1.4.1. 分类 1.4.1.1. 多元分类 1.4.1.2. 分数和概率 1.4.1.3. 不平衡的问题 1.4.2. 回归...
sklearn.model_selection: Model Selection sklearn.datasets: Datasets sklearn.decomposition: Matrix Decomposition sklearn.dummy: Dummyestimators sklearn.ensemble: Ensemble Methods sklearn.exceptions: Exceptions and warnings sklearn.feature_extraction: Feature Extraction ...