import numpy as np import matplotlib.pyplot as plt from sklearn.svm import LinearSVC from sklearn.linear_model import Lasso from sklearn.model_selection import train_test_split from sklearn.feature_selection import SelectFromModel from sklearn.datase…

from sklearn.svm import LinearSVC from sklearn.datasets import load_iris from sklearn.feature_selection import RFE,RFECV from sklearn.model_selection import train_test_split #数据预处理包裹式特征选取RFE模型 def test_RFE(): iris=load_iris() X=iris.data y=iris.targe…

from sklearn.feature_selection import SelectPercentile,f_classif #数据预处理过滤式特征选取SelectPercentile模型 def test_SelectKBest(): X=[[1,2,3,4,5], [5,4,3,2,1], [3,3,3,3,3,], [1,1,1,1,1]] y=[0,1,0,1] print("before transform:",X) selector=SelectPercentile(s…

from sklearn.feature_selection import VarianceThreshold #数据预处理过滤式特征选取VarianceThreshold模型 def test_VarianceThreshold(): X=[[100,1,2,3], [100,4,5,6], [100,7,8,9], [101,11,12,13]] selector=VarianceThreshold(1) selector.fit(X) print("Variances is %s"…

from sklearn.preprocessing import Normalizer #数据预处理正则化Normalizer模型 def test_Normalizer(): X=[[1,2,3,4,5], [5,4,3,2,1], [1,3,5,2,4,], [2,4,1,3,5]] print("before transform:",X) normalizer=Normalizer(norm='l2') print("after transform:",no…

from sklearn.preprocessing import MaxAbsScaler #数据预处理标准化MaxAbsScaler模型 def test_MaxAbsScaler(): X=[[1,5,1,2,10], [2,6,3,2,7], [3,7,5,6,4,], [4,8,7,8,1]] print("before transform:",X) scaler=MaxAbsScaler() scaler.fit(X) print("scale_ is :&quo…

from sklearn.preprocessing import StandardScaler #数据预处理标准化StandardScaler模型 def test_StandardScaler(): X=[[1,5,1,2,10], [2,6,3,2,7], [3,7,5,6,4,], [4,8,7,8,1]] print("before transform:",X) scaler=StandardScaler() scaler.fit(X) print("scale_…

from sklearn.preprocessing import MinMaxScaler #数据预处理标准化MinMaxScaler模型 def test_MinMaxScaler(): X=[[1,5,1,2,10], [2,6,3,2,7], [3,7,5,6,4,], [4,8,7,8,1]] print("before transform:",X) scaler=MinMaxScaler(feature_range=(0,2)) scaler.fit(X) print(&q…

from sklearn.preprocessing import OneHotEncoder #数据预处理二元化OneHotEncoder模型 def test_OneHotEncoder(): X=[[1,2,3,4,5], [5,4,3,2,1], [3,3,3,3,3,], [1,1,1,1,1]] print("before transform:",X) encoder=OneHotEncoder(sparse=False) encoder.fit(X) print(&quo…

from sklearn.preprocessing import Binarizer #数据预处理二元化Binarizer模型 def test_Binarizer(): X=[[1,2,3,4,5], [5,4,3,2,1], [3,3,3,3,3,], [1,1,1,1,1]] print("before transform:",X) binarizer=Binarizer(threshold=2.5) print("after transform:",bin…