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  2. Entropy (information theory) - Wikipedia

    en.wikipedia.org/wiki/Entropy_(information_theory)

    In the view of Jaynes (1957), [19] thermodynamic entropy, as explained by statistical mechanics, should be seen as an application of Shannon's information theory: the thermodynamic entropy is interpreted as being proportional to the amount of further Shannon information needed to define the detailed microscopic state of the system, that remains ...

  3. Information gain ratio - Wikipedia

    en.wikipedia.org/wiki/Information_gain_ratio

    Information gain ratio is used to decide which of the attributes are the most relevant. These will be tested near the root of the tree. One of the input attributes might be the customer's telephone number. This attribute has a high information gain, because it uniquely identifies each customer.

  4. Mutual information - Wikipedia

    en.wikipedia.org/wiki/Mutual_information

    More specifically, it quantifies the "amount of information" (in units such as shannons , nats or hartleys) obtained about one random variable by observing the other random variable. The concept of mutual information is intimately linked to that of entropy of a random variable, a fundamental notion in information theory that quantifies the ...

  5. Information gain (decision tree) - Wikipedia

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

    The expected value of the information gain is the mutual information ⁠ (;) ⁠ of and – i.e. the reduction in the entropy of achieved by learning the state of the random variable . In machine learning, this concept can be used to define a preferred sequence of attributes to investigate to most rapidly narrow down the state of X .

  6. Interaction information - Wikipedia

    en.wikipedia.org/wiki/Interaction_information

    There are many names for interaction information, including amount of information, [1] information correlation, [2] co-information, [3] and simply mutual information. [4] Interaction information expresses the amount of information (redundancy or synergy) bound up in a set of variables, beyond that which is present in any subset of those ...

  7. One in ten rule - Wikipedia

    en.wikipedia.org/wiki/One_in_ten_rule

    For highly correlated input data the one-in-10 rule (10 observations or labels needed per feature) may not be directly applicable due to the high correlation of the features: For images there is a rule of thumb that per class 1000 examples are needed. [11]

  8. Banks and Student Loans: 9 Things You Need To Know - AOL

    www.aol.com/banks-student-loans-9-things...

    Federal student loans limit the amount you can borrow. Limits range from $5,500 yearly for a first-year financially dependent student to $20,500 for graduate and professional students. Private ...

  9. Halting problem - Wikipedia

    en.wikipedia.org/wiki/Halting_problem

    In this abstract framework, there are no resource limitations on the amount of memory or time required for the program's execution; it can take arbitrarily long and use an arbitrary amount of storage space before halting. The question is simply whether the given program will ever halt on a particular input. For example, in pseudocode, the program