Correcting lgb.prepare output comment

R-package/demo/categorical_features_prepare.R

@@ -31,24 +31,24 @@ str(bank)
 # For this task, we use lgb.prepare
 # The function transforms the data into a fittable data
 #
-# Classes 'data.table' and 'data.frame':	4521 obs. of  17 variables:
+# Classes ‘data.table’ and 'data.frame':	4521 obs. of  17 variables:
 # $ age      : int  30 33 35 30 59 35 36 39 41 43 ...
-# $ job      : chr  "unemployed" "services" "management" "management" ...
-# $ marital  : chr  "married" "married" "single" "married" ...
-# $ education: chr  "primary" "secondary" "tertiary" "tertiary" ...
-# $ default  : chr  "no" "no" "no" "no" ...
+# $ job      : num  11 8 5 5 2 5 7 10 3 8 ...
+# $ marital  : num  2 2 3 2 2 3 2 2 2 2 ...
+# $ education: num  1 2 3 3 2 3 3 2 3 1 ...
+# $ default  : num  1 1 1 1 1 1 1 1 1 1 ...
 # $ balance  : int  1787 4789 1350 1476 0 747 307 147 221 -88 ...
-# $ housing  : chr  "no" "yes" "yes" "yes" ...
-# $ loan     : chr  "no" "yes" "no" "yes" ...
-# $ contact  : chr  "cellular" "cellular" "cellular" "unknown" ...
+# $ housing  : num  1 2 2 2 2 1 2 2 2 2 ...
+# $ loan     : num  1 2 1 2 1 1 1 1 1 2 ...
+# $ contact  : num  1 1 1 3 3 1 1 1 3 1 ...
 # $ day      : int  19 11 16 3 5 23 14 6 14 17 ...
-# $ month    : chr  "oct" "may" "apr" "jun" ...
+# $ month    : num  11 9 1 7 9 4 9 9 9 1 ...
 # $ duration : int  79 220 185 199 226 141 341 151 57 313 ...
 # $ campaign : int  1 1 1 4 1 2 1 2 2 1 ...
 # $ pdays    : int  -1 339 330 -1 -1 176 330 -1 -1 147 ...
 # $ previous : int  0 4 1 0 0 3 2 0 0 2 ...
-# $ poutcome : chr  "unknown" "failure" "failure" "unknown" ...
-# $ y        : chr  "no" "no" "no" "no" ...
+# $ poutcome : num  4 1 1 4 4 1 2 4 4 1 ...
+# $ y        : num  1 1 1 1 1 1 1 1 1 1 ...
 bank <- lgb.prepare(data = bank)
 str(bank)