Data come from UCI machine learning repository. Download Data

Use Laplacian Smoothing to make sure probability not be zero. Use Log() to calculate because probability maybe too small and computer can't figure out.

Base on 60 Training Data and 20 Testing Data, and run 10 times to get an average.

MaxRecall: 0.8

MaxPrecision: 1.0

MinError: 0.2

AveRecall: 0.7068421052631579

AvePrecision: 0.9854166666666666

AveError: 0.3

AveTime: 0.028286582971390355

Use Sklearn package.

####C=1 Kernel=‘rbf’

MaxRecall: 1.0

MaxPrecision: 1.0

MinError: 0.05

AveRecall: 0.6505847953216375

AvePrecision: 0.9140598290598291

AveError: 0.425

AveTime: 0.06084513318737096

####Increase C(Penalty parameter)=10.0

MaxRecall: 0.9444444444444444

MaxPrecision: 1.0

MinError: 0.1

AveRecall: 0.8248402869919899

AvePrecision: 0.9303405572755418

AveError: 0.23500000000000004

AveTime: 0.04786081637264809

####C=10.0 Kernel='linear'

MaxRecall: 0.9

MaxPrecision: 1.0

MinError: 0.1

AveRecall: 0.8122222222222222

AvePrecision: 0.977124183006536

AveError: 0.205

AveTime: 0.04208579490100922

####C=10.0 Kernel='sigmoid'

MaxRecall: 1.0

MaxPrecision: 1.0

MinError: 0.2

AveRecall: 0.8083333333333332

AvePrecision: 0.7186842105263158

AveError: 0.47000000000000003

AveTime: 0.0670302688873385

SVM Chart

Use Sklearn package.

####Default n_neighbors=5

MaxRecall: 0.6

MaxPrecision: 1.0

MinError: 0.4

AveRecall: 0.495

AvePrecision: 1.0

AveError: 0.505

AveTime: 0.060209280330714585

####n_neighbors=3, algorithm='ball_tree'

MaxRecall: 0.65

MaxPrecision: 1.0

MinError: 0.35

AveRecall: 0.5499999999999999

AvePrecision: 1.0

AveError: 0.45000000000000007

AveTime: 0.05553739146649517

Based on 60 training data and 20 testing data, there’s a chart:

Conclusion Chart

Based on 120 training data and 40 testing data:

Data Chart

Each algorithm with different data set and different trend.

Time Chart