SK-Learn API 全家福

最近SK-Learn用的比較多， 以后也會經(jīng)常用，將Sk-Learn 所有內(nèi)容整理了一下，整理思路，并可以備查。

（高清圖片可以用鼠標右鍵在單獨窗口打開，或者保存到本地）

基礎(chǔ)公用

base

sklearn.cluster

sklearn.datasets

Loaders

Samples generator

sklearn.exceptions

sklearn.pipeline

sklearn.utils

方法工藝

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)

數(shù)學(xué)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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