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  2. Conditional entropy - Wikipedia

    en.wikipedia.org/wiki/Conditional_entropy

    In information theory, the conditional entropy quantifies the amount of information needed to describe the outcome of a random variable given that the value of another random variable is known. Here, information is measured in shannons , nats , or hartleys .

  3. Information diagram - Wikipedia

    en.wikipedia.org/wiki/Information_diagram

    The violet is the mutual information ⁠ (;) ⁠. Venn diagram of information theoretic measures for three variables x, y , and z . Each circle represents an individual entropy : ⁠ H ( x ) {\displaystyle H(x)} ⁠ is the lower left circle, ⁠ H ( y ) {\displaystyle H(y)} ⁠ the lower right, and ⁠ H ( z ) {\displaystyle H(z)} ⁠ is the ...

  4. Quantities of information - Wikipedia

    en.wikipedia.org/wiki/Quantities_of_information

    Although, in both cases, mutual information expresses the number of bits of information common to the two sources in question, the analogy does not imply identical properties; for example, differential entropy may be negative. The differential analogies of entropy, joint entropy, conditional entropy, and mutual information are defined as follows:

  5. Mutual information - Wikipedia

    en.wikipedia.org/wiki/Mutual_information

    The circle on the right (blue and violet) is (), with the blue being (). The violet is the mutual information I ⁡ ( X ; Y ) {\displaystyle \operatorname {I} (X;Y)} . In probability theory and information theory , the mutual information ( MI ) of two random variables is a measure of the mutual dependence between the two variables.

  6. Joint entropy - Wikipedia

    en.wikipedia.org/wiki/Joint_entropy

    A misleading [1] Venn diagram showing additive, and subtractive relationships between various information measures associated with correlated variables X and Y. The area contained by both circles is the joint entropy H(X,Y). The circle on the left (red and violet) is the individual entropy H(X), with the red being the conditional entropy H(X|Y ...

  7. Information gain (decision tree) - Wikipedia

    en.wikipedia.org/wiki/Information_gain_(decision...

    The relatively high value of entropy () = (1 is the optimal value) suggests that the root node is highly impure and the constituents of the input at the root node would look like the leftmost figure in the above Entropy Diagram. However, such a set of data is good for learning the attributes of the mutations used to split the node.

  8. Line of purples - Wikipedia

    en.wikipedia.org/wiki/Line_of_purples

    The line of purples circled on the CIE chromaticity diagram. In color theory, the line of purples or purple boundary is the locus on the edge of the chromaticity diagram formed between extreme spectral red and violet. Except for these endpoints of the line, colors on the line are non-spectral (no monochromatic light source can generate them).

  9. File:Entropy-diagram.png - Wikipedia

    en.wikipedia.org/wiki/File:Entropy-diagram.png

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