Machine Learning using Boosting Regression in JASP free software | Supervised learning

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  • เผยแพร่เมื่อ 29 ธ.ค. 2024

ความคิดเห็น • 21

  • @haroldasraz
    @haroldasraz 11 หลายเดือนก่อน +1

    Thank you for an excellent presentation. JASP has become extremely impressive. The team and community behind Jamovi and JASP are making a statistically significant software of a gem.

    • @haroldasraz
      @haroldasraz 10 หลายเดือนก่อน

      Can you specify which subjects the model should use? Let's say I trained my model and collected approximately 20 subjects, and now I want to see how well the model predicts disease for this sample whilst being trained on the prior data set.

  • @TravelerTriumphs
    @TravelerTriumphs ปีที่แล้ว +3

    I am moving from SPSS to JASP, and will be using this for my quantitative analysis callses to my MA sociology students, found your presentation really useful.

  • @xiaoyulan8662
    @xiaoyulan8662 2 ปีที่แล้ว +3

    Really amazing presentation and clear interpretation! Thank you. is there any chance of sharing the ppt slides you employed in the presentation?

  • @ZZU06
    @ZZU06 3 ปีที่แล้ว +1

    Sir i have watched your video and totally agreed with you that there is no rule of thumb.. According to my littlr knowledge what i usually intrepret as a statistician is i compare the MAE OR MSE sets of different input combination and look minimal value.

  • @uzairbaig8372
    @uzairbaig8372 2 ปีที่แล้ว +1

    How can we use prediction algorithm for boosting regression in latest JASP version

  • @hemantz1001
    @hemantz1001 3 ปีที่แล้ว +2

    Can you upload a video on random forest ML technique on JASP platform

  • @panpanyang8938
    @panpanyang8938 3 ปีที่แล้ว +1

    Quite brilliant!

  • @haroldasraz
    @haroldasraz 11 หลายเดือนก่อน

    Once you have trained a model. How can you test it on new test data?

  • @buraktiras93
    @buraktiras93 2 ปีที่แล้ว

    Really good presentation, thank you for that! My question is, let's say we completed the training and testing part and saw that we have a model that has high accuracy. How can we deploy it to make further predictions with new inputs?

    • @VahidAryadoust
      @VahidAryadoust  2 ปีที่แล้ว

      Models cannot be deployed from JASP, as far as I know. It is an area for improvement, which you can bring up with the software developer.

  • @Niculuzzu
    @Niculuzzu 3 ปีที่แล้ว

    Spettacolare

  • @黃威綸-w9p
    @黃威綸-w9p 3 ปีที่แล้ว

    Really useful, but how can we apply the model into new data prediction even we do not have the correct answer. thanks your time.

    • @VahidAryadoust
      @VahidAryadoust  3 ปีที่แล้ว

      Sorry could you rephrase the question? I have difficulty understanding it.

    • @黃威綸-w9p
      @黃威綸-w9p 3 ปีที่แล้ว

      @@VahidAryadoust Sorry for unclear question. For example, we use dataset A generate a machine learning model B in JASP. Is it possible to use model B to do prediction in other dataset while we do not have correct answer.

    • @VahidAryadoust
      @VahidAryadoust  3 ปีที่แล้ว +1

      You should feed the new dataset as your left-out (testing) data. Simply create a new variable in the data (call it test-train). Test = 1 and train =2. Replace the test data with your new data and run the analysis again.

    • @uzairbaig8372
      @uzairbaig8372 2 ปีที่แล้ว

      I did not understand. can u please share a short clip. It will be very much helpful for me. Thanks

  • @fernandojosearaujodasilva1918
    @fernandojosearaujodasilva1918 3 ปีที่แล้ว

    Brilliant

  • @kamilbaszczynski8649
    @kamilbaszczynski8649 3 ปีที่แล้ว

    If I may, I have a question. Do continious predictors need to be on similar standardized or normalized scale in order to compare their relative influence on the model?

    • @VahidAryadoust
      @VahidAryadoust  3 ปีที่แล้ว

      I suggest you standardize the variables if they are not on the same scale. For example, standardize them on mean = 0 and SD = 1, or other ways.

    • @kamilbaszczynski8649
      @kamilbaszczynski8649 3 ปีที่แล้ว

      @@VahidAryadoust Thank You for the answer and what about bi- and multi-nomial qualitative data? If I use variables measurer on different types of scales. You would also recommend unification? So for example min-max conversion of all variables?