Matière: Apprentissage automatique II
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Using a Decision Tree, find the suitable candidate to recruit,
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Using a Decision Tree, find the suitable candidate to recruit, based on their performance
Candidates: Gender Edu Level Marital Performance Ahmed : Male Master Divorced ??? Amel : Female Doctorate Single ???
Employees: Tarek : Male Bachelor Single Good Asma : Female Master Married Bad Rabah : Male Doctorate Married Good Hana : Female Master Single Normal Ali : Male Bachelor Divorced Good Fateh : Male Master Married Normal Amina : Female Master Divorced Good Ibrahim: Male Bachelor Married Low
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Posté le 09:34, Saturday 9 Nov 2019 By Imed BOUCHRIKA In Apprentissage automatique II
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Réponses (2)
Réponse (1)
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- Construct our decision tree model:
- Extract rules and calculate their information gain:
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- gender = male => p = 5/8, E(gender=male) = - (1/5) log2(1/5) - (1/5)log2(1/5) - (3/5)log2(3/5) = 1.3710
- gender = female => p= 3/8, E(gender=female) = -1/3 log2(1/3) - 1/3 log(1/3) - 1/3 log(1/3) = 1.5850
- E(Parent) = -4/8 log2(4/8) - 1/8 log2(1/8) - 2/8 log2(2/8) -1/8log2(1/8) = 1.75
- Information Gain(Gender) = 1.75 - ((5/8*1.3710)+(3/8*1.5750)) = 0.3025
- Edu level:
- EL = Bachelor => p = 3/8, E(EL=Bachelor) = -1/3 log2(1/3) - 2/3log2(2/3) = 0.9183
- EL = Master => p= 4/8, E(EL=Master) = -1/4 log2(1/4) -2/4 log2(2/4) - 1/4 log2(1/4) = 1.5
- EL = Doctorate => 1/8 E(EL= doctorate) = 0
- E(Parnet) = 1.75
- Information Gain = 1.75 - (3/8*0.9183+4/8*1.5+1/8*0) = 0.6556
- Marital:
- Marital = Single => p=2/8, E(M=S) = -1/2 log2(1/2) - 1/2 log2(1/2) = 1
- Marital = Married => p=4/8, E(M=Married) = -1/4log2(1/4)*4= 2
- Marital = Divorced => p= 2/8, E(M=Divorced) = 0
- E(Parent) = 1.75
- Information Gain = 1.75 - (2/8*1+4/8*2+2/8*0) = 0.50
First feautre that give us the highest value of information gain is Marital:So the first node in our tree is Marital: If( marital = Divorced ) The performance is always Good :(so the decision tree should look like this):

If (marital = Married) we calculate the other two feautres's information gain:
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- Marital = Married => 4 rows:
- Gender :
- G = Male => P = 3/4, e(G=male) = -1/3 log2(1/3) * 3 = 1.5850
- G = Female => p = 1/4 e(G=Female) = 0
- E(Parent) = E(Marital=Married) = 2
- Information Gain = 2-(3/4*1.5850 + 1/4*0) = 0.8113
- Education Level :
- EL = Master => p = 2/4, E(EL=master) = 1
- EL = Doctorate =>p=1/4, E(EL=Doctorate) = 0
- EL = Bachelor => p=1/4, E(EL=Bachelor) = 0
- E(Parent ) = E(Marital=Married) = 2
- Information Gain = 2- (2/4*1+1/4*0+1/4*0) = 1.5
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- The Education level features has the highest value so our Tree should be like this:

If (marital = Single) we calculate the other two feautres's information gain:
- P(Marital = Single ) = 2
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- Gender:
- Gender = Male-> p= 1/2, E(Gender=Male) =0
- Gender = Female -> p = 1/2 , E(Gender = Female ) = 0
- E(Parent) = E(Marital=Single) = 1
- Information Gain = 1-0 = 1
- Education Level
- EL = Bachelor => p= 1/2, E = 0
- EL = Master=> p= 1/2, E = 0
- Information Gain = 1-0 = 1
- As we see they both have the same information gain so the rank doesn't matter so we draw our tree:

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- If Marital = Married And Education Level = Bachelor -> The performance is always = Low
- If Marital = Married And Education Level = Master ->
- if gender = Male => The performance = Normal
- if gender = Female=> The performance = Bad
- If Marital = Married And Education Level = Doctorate -> The performance is Good
our tree should look like this:

For the last node (Marital = Single) :
- if ( EL= Bachelor) or (Gender = Male) Performance = Good
- if(El = master) or (Gender = Female) Performance = Normal
Final Decition tree :

II. Parse the new data into the decistion tree model:
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- Ahmed:
- Marital = Divorced ===> Performance = Good (based on the training data)
- Amel:
- Marital = Single ----> Gender = Female =====> Performance = Normal
So Ahmed is the suitable candidate to recruit.
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Posté le 16:01, Saturday 9 Nov 2019 by abdennour redjaibia   (282 points) In Apprentissage automatique II
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