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Practical Predictive Analytics by Ralph Winters

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Confidence and lift measures

Similar to previously, sort the rules and examine the highest confidence and lift measures:

tmp <- as.data.frame(arules::inspect(head(sort(rulesx, by = "confidence"), 10))) 
 >     lhs                       rhs      support    confidence lift     > 815 {BAG,BOWL,BOX,SIGN}    => {HOLDER} 0.02070941 0.8571429  2.666601 > 631 {BAG,BOTTLE,BOWL}      => {BOX}    0.02181097 0.8510029  2.941890 > 643 {BOWL,HEART,SIGN}      => {HOLDER} 0.02151722 0.8492754  2.642125 > 820 {BAG,BOX,HEART,SIGN}   => {HOLDER} 0.02137035 0.8434783  2.624090 > 632 {BOTTLE,BOWL,BOX}      => {BAG}    0.02181097 0.8413598  3.489734 > 816 {BAG,BOWL,HOLDER,SIGN} => {BOX}    0.02070941 0.8392857  2.901385 > 817 {BOWL,BOX,HOLDER,SIGN} => {BAG}    0.02070941 0.8392857  3.481131 > 707 {BOTTLE,HEART,SIGN} => {HOLDER} 0.02144378 ...

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