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15-781 Machine Learning Recitation 1

. Entropy, Information GainDecision TreeProbability. Entropy. Suppose X can have one of m values? V1, V2, ? Vm What's the smallest possible number of bits, on average, per symbol, needed to transmit a stream of symbols drawn from X's distribution? It'sH(X) = The entropy of X?High Entro

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15-781 Machine Learning Recitation 1

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    1. 15-781 Machine Learning (Recitation 1) By Jimeng Sun 9/15/05

    2. Entropy, Information Gain Decision Tree Probability

    3. Suppose X can have one of m values… V1, V2, … Vm What’s the smallest possible number of bits, on average, per symbol, needed to transmit a stream of symbols drawn from X’s distribution? It’s H(X) = The entropy of X “High Entropy” means X is from a uniform (boring) distribution “Low Entropy” means X is from varied (peaks and valleys) distribution Entropy

    4. Entropy H(*)

    5. Specific Conditional Entropy H(Y|X=v)

    6. Specific Conditional Entropy H(Y|X=v)

    7. Conditional Entropy H(Y|X)

    8. Conditional Entropy

    9. Information Gain

    10. Decision Tree

    12. Tree pruning

    13. Probability

    14. Test your understanding

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