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Open ML course. Assignment №3
Decision trees with a toy task and the UCI Adult dataset.
Deadline: 25.02.2018, 23.59 CET
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3.1. What is the entropy S0 of the initial system?
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1 point
0,966
0,985
0,885
0,764
3.2. Let's split the data with a feature "Looks_handsome". What is the entropy S1 of the left group - the one with "Looks_handsome". What is the entropy S2 in the opposite group? What is information gain (IG) if we consider such a split?
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2 points
S1 = 0,967
S2 = 0,918
IG = 0,128
S1 = 0,811
S2 = 0,826
IG = 0,178
Required
3.3. What is the entropy of a state given by a list balls_left?
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2 points
0,961
0,852
0,975
0,991
3.4. What is the entropy of a fair dice? (where we look at a dice as a system with 6 equally probable states)
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1 point
0
1
2.585
3.125
0.167
3.5. What is the information gain of splitting the initial dataset into balls_left and balls_right?
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2 points
0,182
0,175
0,161
0,158
3.6. What is the test set accuracy of a decision tree with maximum tree depth of 3 and random_state = 17?
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1 point
0,895
0,856
0,788
0,845
3.7. What is the test set accuracy of a decision tree with maximum tree depth of 9 and random_state = 17?
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1 point
0,91
0,859
0,847
0,791
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