Write Find's algorithm and explain with an example
Answer:
Find's Algorithm:
- Initialize h to a most specific hypothesis. h = {'φ','φ','φ','φ','φ','φ'}.
- For each positive example:
- for each attribute in the example:
- if (attribute value = hypothesis value)
- do nothing.
- else
- Replace the hypothesis value with more general constraint '?'.
flow chart of Algorithm:
Example:
Learning Concept: Days on which person enjoy the sport.
Data Set:
Sky | Temp | Humidity | wind | water | Forecast | sport |
Sunny | warm | Normal | Strong | warm | same | Yes |
Sunny | warm | High | Strong | warm | same | Yes |
Rainy | cold | High | Strong | cool | change | No |
Sunny | warm | High | Strong | warm | same | Yes |
Step1:
Initialize h to the most specific hypothesis. h = {'φ','φ','φ','φ','φ','φ'} take the most specific hypothesis as your 1st positive instance.
h={'sunny', 'warm','Normal', 'Strong', 'warm', 'same'}
Step2:
Compare with another positive instance for each attribute, if (attribute value = hypothesis value) do nothing. else replace the hypothesis value with more general constraint '?'.
1. Since instance 2 is also positive so we will compare with it. In instance 2 attribute humidity is changing so we will generalize that attribute.
h={'sunny', 'warm','?', 'Strong', 'warm', 'same'}2. Instance 3 is negative so we will not consider it.
3. Instance 4 is positive and all attribute of instance 4 is similar to the hypothesis.
so based on find's algorithm the hypothesis for Days on which person enjoy the sport is.
h={'sunny', 'warm','?', 'Strong', 'warm', 'same'}
Term Used:
General Hypothesis: If I tell my friend to bring some food he might bring pizza/burger/biryani/dosa any food because I didn't specify anything so he will do a general hypothesis.
Denoted by G ={'?', '?','?','?', '?','?'}
Denoted by G ={'?', '?','?','?', '?','?'}
Specific Hypothesis: If I tell my friend to bring chicken double-layer king size burger from KFC then it is the specific hypothesis.
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