PyCaret - Accelerate your Machine Learning Insights Cycle

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  • Опубліковано 24 жов 2024

КОМЕНТАРІ • 47

  • @ranjanirajamani7565
    @ranjanirajamani7565 4 роки тому +2

    Its a very helpful library. Thank you for the demo.

  • @rampratapa8101
    @rampratapa8101 4 роки тому +1

    Super info bro👍🤝... Loved this pycaret😊

  • @rushikeshbulbule8120
    @rushikeshbulbule8120 4 роки тому +1

    Very powerful module....
    Thanks for this.. . 👌

  • @TechnicallyData
    @TechnicallyData 4 роки тому +3

    As usual, learnt a lot and excellent video.

  • @hari141v
    @hari141v 4 роки тому +1

    very informative video, thank you

  • @shwetaredkar734
    @shwetaredkar734 4 роки тому +1

    Informative and helpful video. I have a request, please make a tutorial on how to perform 10-fold cv and repeated 10 fold cv and calculate all the performance assessment parameters while performing this. Also, how to test an independent dataset on a trained model.

    • @AIEngineeringLife
      @AIEngineeringLife  4 роки тому +1

      Shweta..Sure and that is in the plan.. Have some backlogs to complete before I get there

    • @shwetaredkar734
      @shwetaredkar734 4 роки тому

      @@AIEngineeringLife okay

  • @saurabhjain507
    @saurabhjain507 4 роки тому +1

    Thanks Sri for this quick video. As a Data Scientist, I would be sceptical of using this package. there 's so much abstraction and too little human inference from the plots that the resultant analysis may not be reliable.

    • @AIEngineeringLife
      @AIEngineeringLife  4 роки тому +1

      Saurabh.. this is not a replacement for human but an assisstant.. feature engineering and post hoc analysis is still manual. This just automated the model section and hyperparameter cycle . You can check my video here on how automl can help
      ua-cam.com/video/RyT47SkRhmg/v-deo.html

  • @muhammedsinan3156
    @muhammedsinan3156 4 роки тому +1

    Great content bro.. expecting a flask deployment vedio soon

    • @AIEngineeringLife
      @AIEngineeringLife  4 роки тому

      I already have a flask video. Are you looking for anything in specific?
      You can check my playlist for flask video
      ua-cam.com/play/PL3N9eeOlCrP5PlN1jwOB3jVZE6nYTVswk.html

  • @vaishalibalaji886
    @vaishalibalaji886 4 роки тому +1

    Hi Srivatsan sir, thank you for the demo. I have one doubt. Is this package useful only for classification models?

    • @AIEngineeringLife
      @AIEngineeringLife  4 роки тому

      It supports regression as well unsupervised problem. You can check the model supported on their website - pycaret.org/

  • @ramchandracheke
    @ramchandracheke 4 роки тому +1

    Thank you sir 😊

  • @mukeshkund4465
    @mukeshkund4465 4 роки тому +1

    Nice Video Sir. Just a small question...Compare model command--> All the models readily avail in pycaret or we need to specify them ??

    • @AIEngineeringLife
      @AIEngineeringLife  4 роки тому +1

      It will run with all available models but we can control as well on what to exclude. Also there are methods for individual models to iterate further

    • @mukeshkund4465
      @mukeshkund4465 4 роки тому

      @@AIEngineeringLife thank you..i will explore more for further information

  • @neemadan
    @neemadan 4 роки тому +3

    Great tutorial. I am getting an error "LightGBMError: Do not support non-ASCII characters in feature name" while using pycaret ML library. I found this related GitHub issue and wanted to check if you have across the same issue and got any suggestions. github.com/microsoft/LightGBM/issues/2478. For now, I tried a temporary solution "compare_models(fold=5, blacklist = ['lightgbm'])" and it's working.

    • @AIEngineeringLife
      @AIEngineeringLife  4 роки тому

      Neeraj.. I did not face this issue. Looks more like LightGBM but that has propagated. Pycaret still uses other libraries

  • @mukeshkund4465
    @mukeshkund4465 4 роки тому

    Hi Sir,
    Small doubt here..When we are using Pycaret Setup then churn_setup becomes a tuple..If we want to see all the transformed column name which are transformed with onehot encoding then how we can see all the name ??

  • @balinasuryachandra9337
    @balinasuryachandra9337 4 роки тому +1

    What can we interpret from the AUC plot and precision plot and classification boundary
    How to understand the SHAP value 11:06 of the variable. it has two bars on either side of the line
    What we would say from those.
    I would be happy if you take one complete video on AUC, precession, recall saying why they matter most in for evaluating the model.

    • @AIEngineeringLife
      @AIEngineeringLife  4 роки тому

      Balina.. Will do.. That is in plan in one of future video where I will be walking through metrics and explanation of output end to end. This video is more to show feature of pycaret

    • @balinasuryachandra9337
      @balinasuryachandra9337 4 роки тому

      @@AIEngineeringLife Thanks for the Reply

  • @shaikrasool5520
    @shaikrasool5520 4 роки тому +1

    sir iam trying to impliment pycart in NLP project..
    input-->
    from pycaret.nlp import *
    exp_nlp = setup(data='df', target = 'user_suggestion')
    but iam getting error
    SystemExit: (Type Error): data passed must be of type pandas.DataFrame or list
    my df is PANDAS DATAFRAME.. what could be the reasaon

    • @AIEngineeringLife
      @AIEngineeringLife  4 роки тому

      You have given off in single quotes, remove that one and it must work

  • @sohamkulkarni523
    @sohamkulkarni523 4 роки тому +1

    Very Informative. Could you pls share the .ipynb notebook?

  • @rajaharshachinta
    @rajaharshachinta 4 роки тому +1

    This is a good example, can you add the GitHub link for the demo?

  • @bharatthakur3642
    @bharatthakur3642 4 роки тому +1

    Nice video sir. I am getting memory error when using airnb data boston and seattle . Any solution

    • @AIEngineeringLife
      @AIEngineeringLife  4 роки тому

      Bharat are you running in colab or local?.. see if u can sample if dataset is very large and you dont have lot of memory

    • @bharatthakur3642
      @bharatthakur3642 4 роки тому

      @@AIEngineeringLife running at kaggle

  • @balajivs3206
    @balajivs3206 4 роки тому +1

    Can you share the ipynb link of the document

  • @Thegreatindiaexpedition
    @Thegreatindiaexpedition 4 роки тому +1

    nailed it

  • @Shankarpandala
    @Shankarpandala 4 роки тому +2

    Few months back I have created a similar python library for myself. Thanks for the video.
    This is my package:
    lazypredict.readthedocs.io/en/latest/usage.html

  • @madhu1987ful
    @madhu1987ful 3 роки тому

    where is the github link for code and dataset link?

  • @rahatrezasulemani2862
    @rahatrezasulemani2862 4 роки тому

    Hi,
    How can I create model with my own set of parameters?

    • @AIEngineeringLife
      @AIEngineeringLife  4 роки тому +1

      In this you cannot set parameters except for models to run against. I would suggest to use regular hyper parameter search software in your case

  • @RajaSekharaReddyKaluri
    @RajaSekharaReddyKaluri 4 роки тому

    For eg: The categorical feature column would be [red, blue, green, yellow, orange], but each sample can belong to multiple categories (such as (red, green)).Delimiter can be , or : or ;
    Are such columns also taken care by pycaret when using setup method.
    If yes, what are the arguments that needs to be taken care of.

    • @AIEngineeringLife
      @AIEngineeringLife  4 роки тому

      You mean it is a list column?.. then you have to handle it externally

    • @RajaSekharaReddyKaluri
      @RajaSekharaReddyKaluri 4 роки тому

      @@AIEngineeringLife not a list column.
      Just image column values to be red, blue and green,red,blue.
      Instead of single category for an attribute you now have multiple categories for an attribute.
      Can pycaret handle the above?

  • @rameshthamizhselvan2458
    @rameshthamizhselvan2458 4 роки тому +1

    Hahahahaa.... ML engineers are not required....

    • @AIEngineeringLife
      @AIEngineeringLife  4 роки тому +2

      I would not go to that extent :).. but it does automate some of boring work that happens in ML cycle. We still need to do feature engineering and post hoc analysis manually

    • @yogeshgautam4762
      @yogeshgautam4762 4 роки тому

      @@AIEngineeringLife sir, means no need to dive into models and hyperparameter tuning, please clear the confusion