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  • JMP will suspend normal business operations for our Winter Holiday beginning on Wednesday, Dec. 24, 2025, at 5:00 p.m. ET (2:00 p.m. ET for JMP Accounts Receivable).
    Regular business hours will resume at 9:00 a.m. EST on Friday, Jan. 2, 2026.
  • We’re retiring the File Exchange at the end of this year. The JMP Marketplace is now your destination for add-ins and extensions.
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Latest Discussions

  • How do use JSL to quickly compare two sets of range columns in order to get the minimum of each column?

    The title is a bit hard to understand. Take this big class as an example:
    1. First, summarize the average values of "height" and "weight" by age.
    2. Then summarize the median of "height" and "weight" by age.
    3. Then compare the mean and median of "height" and take their minimum.
    Finally, compare the mean and median of "body weight" and take their minimum. I'll use the following code and finally loop t...

    lwx228 lwx228
    Discussions |
    Jun 4, 2020 3:49 PM
    3098 views | 4 replies
  • K fold

    Hi!How can I perform a KFold cross-validation with neural networks model by using JMP 15.1?.
    Thanks in advance!

    Marlly_Guarin Marlly_Guarin
    Discussions |
    Jun 2, 2020 10:10 AM
    1999 views | 1 replies
  • How do I auto-calculate precision and F1 score for models on imbalanced data?

    I like that JMP's prints a confusion matrix and ROC for the logistic (Generalized) regression, but some important metrics seem to be missing from the results: precision and F1 score. Is there a way to auto-include these in the output? Or, have it be computed as "automatically" as possible when I re-run a new classificaiton model. I am trying to avoid manually recomputing these with each new model....

    Oleg Oleg
    Discussions |
    Sep 28, 2017 3:40 PM
    20666 views | 16 replies

Latest Discussions

  • How do I auto-calculate precision and F1 score for models on imbalanced data?

    I like that JMP's prints a confusion matrix and ROC for the logistic (Generalized) regression, but some important metrics seem to be missing from the results: precision and F1 score. Is there a way to auto-include these in the output? Or, have it be computed as "automatically" as possible when I re-run a new classificaiton model. I am trying to avoid manually recomputing these with each new model....

    Oleg Oleg
    Discussions |
    Sep 28, 2017 3:40 PM
    20666 views | 16 replies

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