【sklearn-api】api汇总
企业开发
2022-05-14 00:02:02
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sklearn-api 大类汇总
sklearn类 |
En |
Zh |
sklearn.base |
Base classes and utility functions |
基础类 & 工具类 |
sklearn.calibration |
Probability Calibration |
概率 |
sklearn.cluster |
Clustering |
聚类 |
sklearn.compose |
Composite Estimators |
复合 |
sklearn.covariance |
Covariance Estimators |
协方差 |
sklearn.cross_decomposition |
Cross decomposition |
交叉分解 |
sklearn.datasets |
Datasets |
数据集 |
sklearn.decomposition |
Matrix Decomposition |
矩阵分解 |
skleran.discriminant_analysis |
Discriminant Analysis |
判别分析 |
skleran.dummy |
Dummy estimators |
虚拟估计器 |
skleran.ensemble |
Ensemble Methods |
集成方法 |
skleran.exceptions |
Exceptions and warnings |
异常 |
sklearn.experimental |
Experimental |
实验 |
skleran.feature_extraction |
Feature Extraction |
特征提取 |
skleran.feature_selection |
Feature Selection |
特征选择 |
skleran.gaussian_process |
Gaussian Processes |
高斯过程 |
skleran.impute |
Impute |
插补 |
skleran.inspection |
Inspection |
检查 |
skleran.isotonic |
Isotonic regression |
等渗回归 |
skleran.kernel_approximation |
Kernel Approximation |
内核近似 |
skleran.kernel_ridge |
Kernel Ridge Regression |
内核岭回归 |
skleran.linear_model |
Linear Models |
内核岭回归 |
skleran.manifold |
Manifold Learning |
流形学习 |
sklearn.metrics |
Metrics |
指标 |
skleran.mixture |
Gaussian Mixture Models |
高斯混合模型 |
sklearn.model_selection |
Model Selection |
模型选择 |
sklearn.multiclass |
Multiclass classification |
多类和多标签分类 |
sklearn.multioutput |
Multioutput regression and classification |
多输出回归和分类 |
skleran.naive_bayes |
Naive Bayes |
朴素贝叶斯 |
skleran.neighbors |
Nearest Neighbors |
最近邻 |
skleran.neural_network |
Neural network models |
神经网络模型 |
skleran.pipeline |
Pipeline |
管道 |
skleran.preprocessing |
Preprocessing and Normalization |
预处理和规范化 |
skleran.random_projection |
Random projection |
随机投影 |
skleran.semi_supervised |
Semi-Supervised Learning |
半监督学习 |
skleran.svm |
Support Vector Machines |
支持向量机 |
skleran.tree |
Decision Trees |
决策树 |
skleran.utils |
Utilities |
实用工具 |
转载自blog.csdn.net/u010859970/article/details/123077666