Subset 1
## train.data$number_impressions
## n missing unique Info Mean .05 .10 .25 .50
## 1000 0 48 0.93 5.809 1.0 1.0 1.0 2.0
## .75 .90 .95
## 4.0 8.1 17.0
##
## lowest : 1 2 3 4 5, highest: 112 144 181 198 896
Subset 2
## train.data$number_impressions
## n missing unique Info Mean .05 .10 .25 .50
## 1000 0 40 0.91 4.314 1 1 1 2
## .75 .90 .95
## 4 9 16
##
## lowest : 1 2 3 4 5, highest: 78 80 81 122 146
Subset 3
## train.data$number_impressions
## n missing unique Info Mean .05 .10 .25 .50
## 1000 0 45 0.91 5.168 1 1 1 2
## .75 .90 .95
## 4 11 17
##
## lowest : 1 2 3 4 5, highest: 81 107 121 168 365
Subset 4
## train.data$number_impressions
## n missing unique Info Mean .05 .10 .25 .50
## 1000 0 52 0.93 6.531 1.00 1.00 1.00 2.00
## .75 .90 .95
## 4.00 10.00 20.05
##
## lowest : 1 2 3 4 5, highest: 110 153 204 223 1110
Subset 5
## train.data$number_impressions
## n missing unique Info Mean .05 .10 .25 .50
## 1000 0 35 0.92 4.383 1 1 1 2
## .75 .90 .95
## 4 8 13
##
## lowest : 1 2 3 4 5, highest: 97 114 150 239 300