1 ]=> (learn-dtree restaurant-data restaurant-names)
Training data have mixed classification; splitting on some attribute.
Splitting on alternate yields:
  (no yes)
  (((yes 3) (no 3)) ((no 3) (yes 3)))
The information on this split is: 
  (1. 1.)
Splitting on bar yields:
  (yes no)
  (((no 3) (yes 3)) ((yes 3) (no 3)))
The information on this split is: 
  (1. 1.)
Splitting on fri yields:
  (yes no)
  (((no 3) (yes 2)) ((yes 4) (no 3)))
The information on this split is: 
  (.9709505944546686 .9852281360342516)
Splitting on hungry yields:
  (no yes)
  (((yes 1) (no 4)) ((no 2) (yes 5)))
The information on this split is: 
  (.7219280948873623 .863120568566631)
Splitting on patrons yields:
  (none full some)
  (((no 2)) ((no 4) (yes 2)) ((yes 4)))
The information on this split is: 
  (0 .9182958340544896 0)
Splitting on price yields:
  ($$ $ $$$)
  (((yes 2)) ((no 4) (yes 3)) ((yes 1) (no 2)))
The information on this split is: 
  (0 .9852281360342516 .9182958340544896)
Splitting on rain yields:
  (yes no)
  (((yes 2) (no 2)) ((no 4) (yes 4)))
The information on this split is: 
  (1. 1.)
Splitting on reservation yields:
  (no yes)
  (((no 4) (yes 3)) ((yes 3) (no 2)))
The information on this split is: 
  (.9852281360342516 .9709505944546686)
Splitting on type yields:
  (italian burger thai french)
  (((yes 1) (no 1)) ((no 2) (yes 2)) ((yes 2) (no 2)) ((yes 1) (no 1)))
The information on this split is: 
  (1. 1. 1. 1.)
Splitting on wait-estimate yields:
  (above60 upto30 upto60 upto10)
  (((no 2)) ((yes 1) (no 1)) ((no 1) (yes 1)) ((yes 4) (no 2)))
The information on this split is: 
  (0 1. 1. .9182958340544896)
The information required after splitting on alternate is 1..
The information required after splitting on bar is 1..
The information required after splitting on fri is .9792791603760922.
The information required after splitting on hungry is .8042903712002691.
The information required after splitting on patrons is .4591479170272448.
The information required after splitting on price is .8042903712002692.
The information required after splitting on rain is 1..
The information required after splitting on reservation is .9792791603760922.
The information required after splitting on type is .9999999999999999.
The information required after splitting on wait-estimate is .792481250360578.
The attribute with greatest information gain is patrons.
Now working on the split for the patrons="none" with examples:((no (no no no no none $ no no thai upto10))
 (no (no yes no no none $ yes no burger upto10)))

All data have the same classification of: no.

Now working on the split for the patrons="full" with examples:((yes (yes yes yes yes full $ no no burger upto60))
 (no (yes yes yes yes full $$$ no yes italian upto30))
 (no (no yes yes no full $ yes no burger above60))
 (no (yes no yes no full $$$ no yes french above60))
 (yes (yes no yes yes full $ no no thai upto30))
 (no (yes no no yes full $ no no thai upto60)))

Training data have mixed classification; splitting on some attribute.
Splitting on alternate yields:
  (no yes)
  (((no 1)) ((yes 2) (no 3)))
The information on this split is: 
  (0 .9709505944546686)
Splitting on bar yields:
  (no yes)
  (((yes 1) (no 2)) ((yes 1) (no 2)))
The information on this split is: 
  (.9182958340544896 .9182958340544896)
Splitting on fri yields:
  (no yes)
  (((no 1)) ((no 3) (yes 2)))
The information on this split is: 
  (0 .9709505944546686)
Splitting on hungry yields:
  (no yes)
  (((no 2)) ((yes 2) (no 2)))
The information on this split is: 
  (0 1.)
Splitting on price yields:
  ($$$ $)
  (((no 2)) ((yes 2) (no 2)))
The information on this split is: 
  (0 1.)
Splitting on rain yields:
  (yes no)
  (((no 1)) ((yes 2) (no 3)))
The information on this split is: 
  (0 .9709505944546686)
Splitting on reservation yields:
  (yes no)
  (((no 2)) ((yes 2) (no 2)))
The information on this split is: 
  (0 1.)
Splitting on type yields:
  (thai french italian burger)
  (((yes 1) (no 1)) ((no 1)) ((no 1)) ((yes 1) (no 1)))
The information on this split is: 
  (1. 0 0 1.)
Splitting on wait-estimate yields:
  (above60 upto30 upto60)
  (((no 2)) ((no 1) (yes 1)) ((yes 1) (no 1)))
The information on this split is: 
  (0 1. 1.)
The information required after splitting on alternate is .8091254953788906.
The information required after splitting on bar is .9182958340544896.
The information required after splitting on fri is .8091254953788906.
The information required after splitting on hungry is .6666666666666666.
The information required after splitting on price is .6666666666666666.
The information required after splitting on rain is .8091254953788906.
The information required after splitting on reservation is .6666666666666666.
The information required after splitting on type is .6666666666666666.
The information required after splitting on wait-estimate is .6666666666666666.
The attribute with greatest information gain is hungry.
Now working on the split for the hungry="no" with examples:((no (yes no yes no full $$$ no yes french above60))
 (no (no yes yes no full $ yes no burger above60)))

All data have the same classification of: no.

Now working on the split for the hungry="yes" with examples:((no (yes no no yes full $ no no thai upto60))
 (yes (yes no yes yes full $ no no thai upto30))
 (no (yes yes yes yes full $$$ no yes italian upto30))
 (yes (yes yes yes yes full $ no no burger upto60)))

Training data have mixed classification; splitting on some attribute.
Splitting on alternate yields:
  (yes)
  (((no 2) (yes 2)))
The information on this split is: 
  (1.)
Splitting on bar yields:
  (yes no)
  (((no 1) (yes 1)) ((no 1) (yes 1)))
The information on this split is: 
  (1. 1.)
Splitting on fri yields:
  (yes no)
  (((no 1) (yes 2)) ((no 1)))
The information on this split is: 
  (.9182958340544896 0)
Splitting on price yields:
  ($$$ $)
  (((no 1)) ((no 1) (yes 2)))
The information on this split is: 
  (0 .9182958340544896)
Splitting on rain yields:
  (no)
  (((no 2) (yes 2)))
The information on this split is: 
  (1.)
Splitting on reservation yields:
  (yes no)
  (((no 1)) ((no 1) (yes 2)))
The information on this split is: 
  (0 .9182958340544896)
Splitting on type yields:
  (burger italian thai)
  (((yes 1)) ((no 1)) ((no 1) (yes 1)))
The information on this split is: 
  (0 0 1.)
Splitting on wait-estimate yields:
  (upto30 upto60)
  (((yes 1) (no 1)) ((no 1) (yes 1)))
The information on this split is: 
  (1. 1.)
The information required after splitting on alternate is 1..
The information required after splitting on bar is 1..
The information required after splitting on fri is .6887218755408672.
The information required after splitting on price is .6887218755408672.
The information required after splitting on rain is 1..
The information required after splitting on reservation is .6887218755408672.
The information required after splitting on type is .5.
The information required after splitting on wait-estimate is 1..
The attribute with greatest information gain is type.
Now working on the split for the type="burger" with examples:((yes (yes yes yes yes full $ no no burger upto60)))

All data have the same classification of: yes.

Now working on the split for the type="italian" with examples:((no (yes yes yes yes full $$$ no yes italian upto30)))

All data have the same classification of: no.

Now working on the split for the type="thai" with examples:((yes (yes no yes yes full $ no no thai upto30))
 (no (yes no no yes full $ no no thai upto60)))

Training data have mixed classification; splitting on some attribute.
Splitting on alternate yields:
  (yes)
  (((yes 1) (no 1)))
The information on this split is: 
  (1.)
Splitting on bar yields:
  (no)
  (((yes 1) (no 1)))
The information on this split is: 
  (1.)
Splitting on fri yields:
  (no yes)
  (((no 1)) ((yes 1)))
The information on this split is: 
  (0 0)
Splitting on price yields:
  ($)
  (((yes 1) (no 1)))
The information on this split is: 
  (1.)
Splitting on rain yields:
  (no)
  (((yes 1) (no 1)))
The information on this split is: 
  (1.)
Splitting on reservation yields:
  (no)
  (((yes 1) (no 1)))
The information on this split is: 
  (1.)
Splitting on wait-estimate yields:
  (upto60 upto30)
  (((no 1)) ((yes 1)))
The information on this split is: 
  (0 0)
The information required after splitting on alternate is 1..
The information required after splitting on bar is 1..
The information required after splitting on fri is 0.
The information required after splitting on price is 1..
The information required after splitting on rain is 1..
The information required after splitting on reservation is 1..
The information required after splitting on wait-estimate is 0.
The attribute with greatest information gain is fri.
Now working on the split for the fri="no" with examples:((no (yes no no yes full $ no no thai upto60)))

All data have the same classification of: no.

Now working on the split for the fri="yes" with examples:((yes (yes no yes yes full $ no no thai upto30)))

All data have the same classification of: yes.

Now working on the split for the patrons="some" with examples:((yes (no no no yes some $$ yes yes thai upto10))
 (yes (no yes no yes some $$ yes yes italian upto10))
 (yes (no yes no no some $ no no burger upto10))
 (yes (yes no no yes some $$$ no yes french upto10)))

All data have the same classification of: yes.

;Value 1: (patrons (none no) (full (hungry (no no) (yes (type (burger yes) (italian no) (thai (fri (no no) (yes yes))))))) (some yes))