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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