Final figure Classify it Classify it.

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Presentation transcript:

Final figure Classify it Classify it

Classification I Classify it P(play=yes|outlook=sunny, temp=cool,humidity=high, windy=true) = *P(play=yes) *P(outlook=sunny|play=yes) *P(temp=cool|play=yes, outlook=sunny) *P(humidity=high|play=yes, temp=cool) *P(windy=true|play=yes, = *0.625*0.25*0.4*0.2*0.5 = *0.00625

Classification II Classify it P(play=no|outlook=sunny, temp=cool,humidity=high, windy=true) = *P(play=no) *P(outlook=sunny|play=no) *P(temp=cool|play=no, outlook=sunny) *P(humidity=high|play= no, temp=cool) *P(windy=true|play=no, = *0.375*0.5*0.167*0.333*0.4 = *0.00417

Classification III Classify it P(play=yes|outlook=sunny, temp=cool,humidity=high, windy=true) = *0.00625 P(play=no|outlook=sunny, temp=cool,humidity=high, windy=true) = *.00417  = 1/(0.00625+0.00417) =95.969 = 95.969*0.00625 = 0.60

Classification IV (missing values or hidden variables) P(play=yes|temp=cool, humidity=high, windy=true) = *outlookP(play=yes) *P(outlook|play=yes) *P(temp=cool|play=yes,outlook) *P(humidity=high|play=yes, temp=cool) *P(windy=true|play=yes,outlook) =…(next slide)

Classification V (missing values or hidden variables) P(play=yes|temp=cool, humidity=high, windy=true) = *outlookP(play=yes)*P(outlook|play=yes)*P(temp=cool|play=yes,outlook) *P(humidity=high|play=yes,temp=cool)*P(windy=true|play=yes,outlook) = *[ P(play=yes)*P(outlook= sunny|play=yes)*P(temp=cool|play=yes,outlook=sunny) *P(humidity=high|play=yes,temp=cool)*P(windy=true|play=yes,outlook=sunny) +P(play=yes)*P(outlook= overcast|play=yes)*P(temp=cool|play=yes,outlook=overcast) *P(humidity=high|play=yes,temp=cool)*P(windy=true|play=yes,outlook=overcast) +P(play=yes)*P(outlook= rainy|play=yes)*P(temp=cool|play=yes,outlook=rainy) *P(humidity=high|play=yes,temp=cool)*P(windy=true|play=yes,outlook=rainy) ] 0.625*0.25*0.4*0.2*0.5 + 0.625*0.417*0.286*0.2*0.5 + 0.625*0.33*0.333*0.2*0.2 ] =*0.01645

Classification VI (missing values or hidden variables) P(play=no|temp=cool, humidity=high, windy=true) = *outlookP(play=no)*P(outlook|play=no)*P(temp=cool|play=no,outlook) *P(humidity=high|play=no,temp=cool)*P(windy=true|play=no,outlook) = *[ P(play=no)*P(outlook=sunny|play=no)*P(temp=cool|play=no,outlook=sunny) *P(humidity=high|play=no,temp=cool)*P(windy=true|play=no,outlook=sunny) +P(play=no)*P(outlook= overcast|play=no)*P(temp=cool|play=no,outlook=overcast) *P(humidity=high|play=no,temp=cool)*P(windy=true|play=no,outlook=overcast) +P(play=no)*P(outlook= rainy|play=no)*P(temp=cool|play=no,outlook=rainy) *P(humidity=high|play=no,temp=cool)*P(windy=true|play=no,outlook=rainy) ] 0.375*0.5*0.167*0.333*0.4 + 0.375*0.125*0.333*0.333*0.5 + 0.375*0.375*0.4*0.333*0.75 ] =*0.0208

Classification VII (missing values or hidden variables) P(play=yes|temp=cool, humidity=high, windy=true) =*0.01645 P(play=no|temp=cool, humidity=high, windy=true) =*0.0208 =1/(0.01645 + 0.0208)= 26.846 P(play=yes|temp=cool, humidity=high, windy=true) = 26.846 * 0.01645 = 0.44 P(play=no|temp=cool, humidity=high, windy=true) = 26.846 * 0.0208 = 0.56 I.e. P(play=yes|temp=cool, humidity=high, windy=true) is 44% and P(play=no|temp=cool, humidity=high, windy=true) is 56% So, we predict ‘play=no.’