python庫之——sklearn

機器學習庫sklearn

官方documentation（資料）中分為不同的部分：

 

其中我們主要講User Guide(機器學習算法理論介紹)、API(程序實現方法)：

一、User Guide

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

模塊	說明
Supervised learning監督學習	監督學習的各種算法介紹
Unsupervised learning非監督學習	非監督學習的各種算法介紹
Model selection and evaluation模型選擇和評價	交叉驗證、調參、模型評價、驗證曲線
Inspection檢查
Dataset transformations數據轉換	特征抽取、數據預處理、缺失值處理、非監督降維方法、隨機投影、核近似、轉換預測目標
Dataset loading utilities數據下載程序	玩具數據、真實數據集、生成數據、下載其它數據
Computing with scikit-learn利用sklearn計算	對大數據集的計算策略、計算表現、並行計算、資源管理和配置

二、api

和前面的內容對應，這個內容里給了在sklearn里的實現方法。

模塊

功能

sklearn.base module: Base classes and utility functions sklearn.calibration module: Probability Calibration(標准、標定) sklearn.cluster: Clustering sklearn.cluster.bicluster: Biclustering sklearn.compose: Composite Estimators sklearn.covariance: Covariance Estimators（協方差） sklearn.cross_decomposition: Cross decomposition（交叉分解） sklearn.datasets: Datasets sklearn.decomposition: Matrix Decomposition sklearn.discriminant_analysis: Discriminant Analysis（判別分析） sklearn.dummy: Dummy estimators sklearn.ensemble: Ensemble Methods sklearn.exceptions module(exceptions模塊): 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（實用程序）