SK-Learn API 全家福

   最近SK-Learn用的比较多， 以后也会经常用，将Sk-Learn 所有内容整理了一下，整理思路，并可以备查。

（高清图片可以用鼠标右键在单独窗口打开，或者保存到本地）

基础公用

 base

 sklearn.cluster

 sklearn.datasets

  Loaders

  Samples generator

 sklearn.exceptions

 sklearn.pipeline

 sklearn.utils

方法工艺

 sklearn.cluster

  classes

  Functions

 sklearn.cluster.bicluster

 sklearn.model_selection

  Splitter Classes

  Splitter Functions

  Hyper-parameter optimizers

  Model validation

 sklearn.dummy

 sklearn.ensemble(Ensemble Methods)

 sklearn.feature_extraction

 sklearn.feature_selection

 sklearn.gaussian_process

 sklearn.metrics

  Model Selection Interface

  Classification metrics

  Regression metrics

  Multilabel ranking metrics

  Clustering metrics

  Biclustering metrics

  Pairwise metrics

 sklearn.multioutput( Multioutput regression and classification)

 sklearn.calibration ( Probability Calibration)

 sklearn.cross_decomposition ( Cross decomposition )

 sklearn.preprocessing ( Preprocessing and Normalization)

数学算法

 sklearn.covariance

 sklearn.decomposition

 sklearn.isotonic

 sklearn.kernel_approximation

 sklearn.kernel_ridge

 sklearn.discriminant_analysis

 sklearn.linear_model ( Generalized Linear Models )

 sklearn.manifold

 sklearn.mixture( Gaussian Mixture Models )

 sklearn.multiclass

 sklearn.naive_bayes

 sklearn.neighbors

 sklearn.semi_supervised

 sklearn.svm

 sklearn.tree

NN算法

 sklearn.neural_network

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