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sklearn實戰-乳腺癌細胞數據挖掘（博主親自錄制視頻）

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sklearn.preprocessing.LabelEncoder()：標準化標簽

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standardScaler==features with a mean=0 and variance=1 minMaxScaler==features in a 0 to 1 range normalizer==feature vector to a euclidean length=1 normalization bring the values of each feature vector on a common scale L1-least absolute deviations-sum of absolute values(on each row)=1;it is insensitive to outliers L2-Least squares-sum of squares(on each row)=1;takes outliers in consideration during traing # -*- coding: utf-8 -*- """ Created on Sat Apr 14 09:09:41 2018@author:Toby standardScaler==features with a mean=0 and variance=1 minMaxScaler==features in a 0 to 1 range normalizer==feature vector to a euclidean length=1normalization bring the values of each feature vector on a common scale L1-least absolute deviations-sum of absolute values(on each row)=1;it is insensitive to outliers L2-Least squares-sum of squares(on each row)=1;takes outliers in consideration during traing"""from sklearn import preprocessing import numpy as npdata=np.array([[2.2,5.9,-1.8],[5.4,-3.2,-5.1],[-1.9,4.2,3.2]]) bindata=preprocessing.Binarizer(threshold=1.5).transform(data) print('Binarized data:',bindata)#mean removal print('Mean(before)=',data.mean(axis=0)) print('standard deviation(before)=',data.std(axis=0))#features with a mean=0 and variance=1 scaled_data=preprocessing.scale(data) print('Mean(before)=',scaled_data.mean(axis=0)) print('standard deviation(before)=',scaled_data.std(axis=0)) print('scaled_data:',scaled_data) ''' scaled_data: [[ 0.10040991 0.91127074 -0.16607709][ 1.171449 -1.39221918 -1.1332319 ][-1.27185891 0.48094844 1.29930899]] '''#features in a 0 to 1 range minmax_scaler=preprocessing.MinMaxScaler(feature_range=(0,1)) data_minmax=minmax_scaler.fit_transform(data) print('MinMaxScaler applied on the data:',data_minmax) ''' MinMaxScaler applied on the data: [[ 0.56164384 1. 0.39759036][ 1. 0. 0. ][ 0. 0.81318681 1. ]] '''data_l1=preprocessing.normalize(data,norm='l1') data_l2=preprocessing.normalize(data,norm='l2') print('l1-normalized data:',data_l1) ''' [[ 0.22222222 0.5959596 -0.18181818][ 0.39416058 -0.23357664 -0.37226277][-0.20430108 0.4516129 0.34408602]] ''' print('l2-normalized data:',data_l2) ''' [[ 0.3359268 0.90089461 -0.2748492 ][ 0.6676851 -0.39566524 -0.63059148][-0.33858465 0.74845029 0.57024784]] '''

　　

python風控評分卡建模和風控常識

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