Page 132 - Artificial Intellegence_v2.0_Class_12
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4 5 .0 3 .6 1 .4 0 .2 I r is- setosa
5 5 .4 3 .9 1 .7 0 .4 I r is- setosa
6 4 .6 3 .4 1 .4 0 .3 I r is- setosa
7 5 .0 3 .4 1 .5 0 .2 I r is- setosa
8 4 .4 2 .9 1 .4 0 .2 I r is- setosa
9 4 .9 3 .1 1 .5 0 .1 I r is- setosa
1 0 5 .4 3 .7 1 .5 0 .2 I r is- setosa
1 1 4 .8 3 .4 1 .6 0 .2 I r is- setosa
1 2 4 .8 3 .0 1 .4 0 .1 I r is- setosa
1 3 4 .3 3 .0 1 .1 0 .1 I r is- setosa
1 4 5 .8 4 .0 1 .2 0 .2 I r is- setosa
1 5 5 .7 4 .4 1 .5 0 .4 I r is- setosa
1 6 5 .4 3 .9 1 .3 0 .4 I r is- setosa
1 7 5 .1 3 .5 1 .4 0 .3 I r is- setosa
1 8 5 .7 3 .8 1 .7 0 .3 I r is- setosa
1 9 5 .1 3 .8 1 .5 0 .3 I r is- setosa
sep al- length sep al- w id th p etal- length p etal - w id th
cou nt 1 5 0 .0 0 0 0 0 0 1 5 0 .0 0 0 0 0 0 1 5 0 .0 0 0 0 0 0 1 5 0 .0 0 0 0 0 0
mean 5 .8 4 3 3 3 3 3 .0 5 4 0 0 0 3 .7 5 8 6 6 7 1 .1 9 8 6 6 7
std 0 .8 2 8 0 6 6 0 .4 3 3 5 9 4 1 .7 6 4 4 2 0 0 .7 6 3 1 6 1
min 4 .3 0 0 0 0 0 2 .0 0 0 0 0 0 1 .0 0 0 0 0 0 0 .1 0 0 0 0 0
2 5 % 5 .1 0 0 0 0 0 2 .8 0 0 0 0 0 1 .6 0 0 0 0 0 0 .3 0 0 0 0 0
5 0 % 5 .8 0 0 0 0 0 3 .0 0 0 0 0 0 4 .3 5 0 0 0 0 1 .3 0 0 0 0 0
7 5 % 6 .4 0 0 0 0 0 3 .3 0 0 0 0 0 5 .1 0 0 0 0 0 1 .8 0 0 0 0 0
max 7 .9 0 0 0 0 0 4 .4 0 0 0 0 0 6 .9 0 0 0 0 0 2 .5 0 0 0 0 0
# sp lit- ou t v alid ation d ataset
ar r ay = d ataset.v alu es
# p r int( ar r ay )
X = ar r ay [ : , 0 : 4 ]
Y = ar r ay [ : , 4 ]
X _ tr ain, X _ v alid ation, Y _ tr ain, Y _ v alid ation= tr ain_ test_ sp lit( X , Y , test_ siz e= 0 .2 , tr ain_
siz e= 0 .8 ,shuffle=T r u e )
# imp or t an algor ithm and tr ain d ata
mod elT oI mp lement= L ogisticRegr ession( solv er = ' lib linear ' , mu lti_ class= ' ov r ' )
modelToImplement.fit(X_train,Y_train) # p er f or m tr aining
p r ed iction= mod elT oI mp lement.p r ed ict( [ [ 3 .0 , 2 .0 , 2 .5 , 0 .2 ] ] ) # p r ed iction w ith new v alu es
p r int p r ed iction)
(
Touchpad Artificial Intelligence (Ver. 2.0)-XII

