python库之——sklearn

机器学习库sklearn

官方documentation（资料）中分为不同的部分：

其中我们主要讲User Guide(机器学习算法理论介绍)、API(程序实现方法)：

一、User Guide

https://scikit-learn.org/stable/user_guide.html

模块	说明
Supervised learning监督学习	监督学习的各种算法介绍
Unsupervised learning非监督学习	非监督学习的各种算法介绍
Model selection and evaluation模型选择和评价	交叉验证、调参、模型评价、验证曲线
Inspection检查
Dataset transformations数据转换	特征抽取、数据预处理、缺失值处理、非监督降维方法、随机投影、核近似、转换预测目标
Dataset loading utilities数据下载程序	玩具数据、真实数据集、生成数据、下载其它数据
Computing with scikit-learn利用sklearn计算	对大数据集的计算策略、计算表现、并行计算、资源管理和配置

二、api

和前面的内容对应，这个内容里给了在sklearn里的实现方法。

模块

功能

sklearn.base module: Base classes and utility functions sklearn.calibration module: Probability Calibration(标准、标定) sklearn.cluster: Clustering sklearn.cluster.bicluster: Biclustering sklearn.compose: Composite Estimators sklearn.covariance: Covariance Estimators（协方差） sklearn.cross_decomposition: Cross decomposition（交叉分解） sklearn.datasets: Datasets sklearn.decomposition: Matrix Decomposition sklearn.discriminant_analysis: Discriminant Analysis（判别分析） sklearn.dummy: Dummy estimators sklearn.ensemble: Ensemble Methods sklearn.exceptions module(exceptions模块): Exceptions and warnings sklearn.experimental: Experimental sklearn.feature_extraction: Feature Extraction sklearn.feature_selection: Feature Selection sklearn.gaussian_process: Gaussian Processes sklearn.isotonic: Isotonic regression sklearn.impute: Impute sklearn.kernel_approximation Kernel Approximation sklearn.kernel_ridge Kernel Ridge Regression sklearn.linear_model: Generalized Linear Models? sklearn.manifold: Manifold Learning sklearn.metrics: Metrics sklearn.mixture: Gaussian Mixture Models sklearn.model_selection: Model Selection sklearn.multiclass: Multiclass and multilabel classification sklearn.multioutput: Multioutput regression and classification sklearn.naive_bayes: Naive Bayes sklearn.neighbors: Nearest Neighbors sklearn.neural_network: Neural network models sklearn.pipeline: Pipeline sklearn.inspection: inspection sklearn.preprocessing: Preprocessing and Normalization sklearn.random_projection: Random projection? sklearn.random_projection: Random projection? sklearn.svm: Support Vector Machines? sklearn.tree: Decision Trees? sklearn.utils: Utilities（实用程序）