On Sunday, October 2, 2016 at 2:12:39 AM UTC+1, 38016...@gmail.com wrote:
> I am trying to print a simple decision tree for my homework.
> The answer must keep in this format:
>
> Top 7,4,0.95
> career gain = 100
> 1.Management 2, 3, 0.9709505944546686
> 2.Service 5, 1, 0.6500224216483541
> location gain = 100
> 1.Oregon 4, 1, 0.7219280948873623
> 2.California 3, 3, 1.0
> edu_level gain = 100
> 1.High School 5, 1, 0.6500224216483541
> 2.College 2, 3, 0.9709505944546686
> years_exp gain = 100
> 1.Less than 3 3, 1, 0.8112781244591328
> 2.3 to 10 2, 1, 0.9182958340544896
> 3.More than 10 2, 2, 1.0
>
> Here is my code:
> features={'edu_level':['High School','College'],'career':
> ['Management','Service'],'years_exp':['Less than 3','3 to 10','More than
> 10'],'location':['Oregon','California']}
>
> print('Top 7,4,0.95')
> for key in features:
> print('{} gain = {}'.format(key,100))
> attributes_list=features[key]
> kargs={}
> for i in range(len(attributes_list)):
> kargs[key]=attributes_list[i]
> low=table.count('Low',**kargs)
> high=table.count('High',**kargs)
> print('\t{}.{} {}, {},
> {}'.format(i+1,attributes_list[i],low,high,entropy(low,high)))
>
> I set all the gain as 100 now.But actually the gain must calculate with the
> data below.
> For example, the career gain need the data of 'Management' and 'Service'.
> I don't know how to do.
> or Anyone can provide me a better logic?
This code cannot run as neither count nor entropy are defined.
You can loop around the features like this:-
for key, attributes_list in features.items():
'iteritems' as suggested by Peter Pearson is Python 2 only.
You can loop around your attributes with:-
for attribute in attributes_list:
If you need the index in the loop use the enumerate function
https://docs.python.org/3/library/functions.html#enumerate
Kindest regards.
Mark Lawrence.
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