Regarding the logical problem of classification, the problem of logical division

Updated on educate 2024-04-08
12 answers
  1. Anonymous users2024-02-07

    Prerequisites: The original proposition has the same truth or falsehood as its inverse negative proposition.

    The original proposition is that a product cannot be classified as C if it does not have feature A or feature B (if it is not A or B, it is not C).

    The negative proposition is: a product can be classified as C if it has feature A and feature B (if A and B, then C) The inverse proposition is: A product cannot be classified as C, there is no feature A or feature B;

    Reverse negative proposition: A product can be classified as C, with feature A or feature B.

    Because: the original proposition has the same truth or falsehood as its inverse negative proposition.

    Therefore, both the original proposition and the inverse proposition are true.

    Therefore, the original proposition can be understood to mean that if a product can be classified as C, then the product has feature A or feature B.

    Going back to the practical problem, the product has feature B, so it is classified as C.

  2. Anonymous users2024-02-06

    Absolutely.

    As long as there is any one of AB, it is considered C

  3. Anonymous users2024-02-05

    Or the relationship, from the known "if 1 or 2, on 3", it can be known that satisfying 1 or satisfying 2 can get 3, and the landlord gives a question that satisfies 1, so it can get 3, that is, it cannot be classified as c

    1 means no feature a; 2 means no feature b; 3 means cannot be classified as c].

  4. Anonymous users2024-02-04

    It's too simple. "Or" means "or", and according to the title, it is: feature A or feature B, and one of the two can be considered to meet the condition of C. Therefore, one of the products in question should be classified as C.

  5. Anonymous users2024-02-03

    This provision is not very clear. It can only be used to determine that a product cannot be classified as a when the product does not have a A and does not have a BHowever, it is not confirmed whether the product can be returned after having A or B.

  6. Anonymous users2024-02-02

    According to the title, it can be seen that one of the features A and B can be considered to meet the conditions of C, so a product in question should be classified as C.

  7. Anonymous users2024-02-01

    It should be possible to attribute the relationship between C, A and B is or, so as long as one of them is satisfied, it is fine.

  8. Anonymous users2024-01-31

    If there is no AB feature, of course, it is classified as something else, and if there is a B feature, it does not mean that it must be C.

  9. Anonymous users2024-01-30

    It is possible to make the mistake of "dividing according to different ones", the former by season, and the latter by time.

    The sum of the extensions of "three months" is less than the extension of "one quarter". Therefore, the logical error of "incomplete division" may be committed.

    "Divide" and "decompose" are the best differences. A genus concept is divided into several species concepts, and each division of the species concept has the connotation of a genus concept. Decomposition is the division of the whole object into its components, and each component does not have to have the connotation of the whole object.

  10. Anonymous users2024-01-29

    Spring, summer, autumn, winter and the year are the relationship between the part and the whole, and the division we are talking about should be the relationship between the parent and the child, which is not a matter of breaking the rules, but not a division at all. (To make a rule is a division error, but it is still a division).

  11. Anonymous users2024-01-28

    The division must be a genus and species relationship, and spring, summer, autumn, winter, and year are the relationship between parts and years, and cannot be divided into ......

  12. Anonymous users2024-01-27

    Logistic regression is a widely used categorical machine learning algorithm that fits data into a logistic function to complete the probability of an event occurring.

    Logical functions: <>

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