SK-Learn API 全家福
最近SK-Learn用的比較多, 以後也會常用,將Sk-Learn 所有內容整理了一下,整理思路,並可備查。
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基礎公用
base
sklearn.cluster
sklearn.datasets
Loaders
Samples generator
sklearn.exceptions# Samples generator
sklearn.exceptions# Samples # sklearn.exceptions
# sklearn.pipeline
sklearn.utils
#方法流程
sklearn.cluster
## classes
Functions
## classes
# Functions
## classes
# Functions
## classes
# Functions
sklearn.cluster.bicluster
sklearn.model_selection
Splitter Classes
Splitter Functions
Hyper-parameter opt#imizers# validation
sklearn.dummy
sklearn.ensemble(Ensemble Methods)
sklearn.feature_extraction
sklearn.feature_selection
ussian_extraction
sklearn.feature_selection##1N;#ussm.
sklearn.metrics
Model Selection Interface
Classification metrics
Regression metrics
Multilabel ranking metrics##CCling metrics
Biclustering metrics
Pairwise metrics
sklearn.multioutput( Multioutput regression and classification)
### # sklearn.cross_decomposition ( Cross decomposition )
sklearn.preprocessing ( Preprocessing and Normalization)
數學演算法
sklearn.covariance
sklearn.decomposition# sklearn.covariance
sklearn.decomposition
## sklearn.isotonic
sklearn.kernel_approximation
sklearn.kernel_ridge
## sklearn.discriminant_analysis
sklearn.linear_#arn.
# sklearn.manifold
sklearn.mixture( Gaussian Mixture Models )
sklearn.multiclass
# sklearn.naive_bayes
skle.neigh## sklearn.naive_bayes
skle.neigh#.
# sklearn.semi_supervised
sklearn.svm
sklearn.tree######NN演算法###### sklearn.neural_network###
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