sklearn-Standardized label Labelencoder

Sklearn.preprocessing.LabelEncoder (): Standardized labeling

Standardscaler==features with a mean=0 and variance=1

Minmaxscaler==features in a 0 to 1 rangenormalizer==feature vector to a Euclidean length=1

Normalizationbring the values of each of the feature vectors on a common scalel1-least absolute deviations-sum of absolute values (  On each row) =1;it are insensitive to outliersl2-least squares-sum of squares (on each row) =1;takes outliers in consideration During traing

#-*-Coding:utf-8-*-"" "Created on Sat April 09:09:41 2018@author:toby standardscaler==features with a mean=0 and Varia Nce=1minmaxscaler==features in a 0 to 1 rangenormalizer==feature vector to a Euclidean length=1normalizationbring the Valu Es of each feature vector in a common scalel1-least absolute deviations-sum of absolute values (on each row) =1;it is Insens Itive to Outliersl2-least squares-sum of squares (on all row) =1;takes outliers in consideration during Traing "" "from Sklea RN Import Preprocessingimport 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 removalprint (' mean (before) = ', Data.mean (axis=0)) print (' Standard deviation (before) = ', DATA.STD (axis=0)) #features with a mean=0 and variance=1scaled_ 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 0.4809484 4 1.29930899]] "#features in a 0 to 1 rangeminmax_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]] "

　　

sklearn-Standardized label Labelencoder