Polynomial Regression in Python - sklearn

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  • čas přidán 11. 06. 2023
  • Please feel free to download the dataset from this link:
    github.com/rashida048/Machine...
    The complete notebook is available here:
    github.com/rashida048/Machine...
    As mentioned in the video, here is the link to the simple linear regression explanation:
    • Simple Linear Regressi...
    Please feel free to check out my Data Science blog where you will find a lot of data visualization, exploratory data analysis, statistical analysis, machine learning, natural language processing, and computer vision tutorials and projects:
    regenerativetoday.com/
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    / rashida048
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    #polynomialregression #machinelearning #datascience #artificialintelligence #dataAnalytics #python #sklearn #jupyternotebook

Komentáře • 18

  • @Landon_R
    @Landon_R Před rokem +2

    You are an excellent teacher. Thank you for your videos.

  • @meshackamimo1945
    @meshackamimo1945 Před měsícem

    wonderful way to simplify a diffcult topic to beginners. keep it up!

  • @VolantData
    @VolantData Před rokem +2

    Amazing explanation! Thank you very much

  • @arunthashapiruthviraj2783
    @arunthashapiruthviraj2783 Před 10 měsíci

    Recommend her for beginer. well structured explanation

  • @maithreyans5892
    @maithreyans5892 Před 2 měsíci

    how do input new input values and predict a value for them

  • @farahmarsusi9670
    @farahmarsusi9670 Před 25 dny

    Thanks for the video. A question: is poly.fit(X_poly_train, y_train) necessary?

  • @ISHMAMBINROFI
    @ISHMAMBINROFI Před rokem

    Will you please upload a tutorial for random forest?

  • @user-jb4zw5gi6c
    @user-jb4zw5gi6c Před 10 měsíci

    Why we used fit_transform for trained data, but just use transform for test data?

    • @regenerativetoday4244
      @regenerativetoday4244  Před 10 měsíci +1

      Because you want to get mean and standard deviation for scaling from training data only and then use that information to transform test data. You don't want to fit in the test data because you want the test data to be totally unknown to the model

  • @maheshmaskey4592
    @maheshmaskey4592 Před 2 měsíci

    Thank you for the excellent post; what about other statistics like R-squared and correlation coefficient?
    Have you thought about the multivariate polynomial equation model? As you mentioned, training is overfitting but validation is very poor. Any suggestions are welcome.

    • @regenerativetoday4244
      @regenerativetoday4244  Před 2 měsíci +2

      If you are using this for a real world project, first try with different polynomial first and if you still do not get good results try other models. Usually for real world projects we try several different models with different parameters and finalize the best one.

    • @maheshmaskey4592
      @maheshmaskey4592 Před 2 měsíci

      @@regenerativetoday4244 Yes, I am using a real-world problem and trying to start with it before trying others.

    • @maheshmaskey4592
      @maheshmaskey4592 Před 2 měsíci

      @@regenerativetoday4244 Actually, I want to establish an empirical equation, as most of the other models are black boxes without equations.

  • @nhanvo5687
    @nhanvo5687 Před 8 měsíci +1

    why using degree = 6, please ?

    • @regenerativetoday4244
      @regenerativetoday4244  Před 2 měsíci +2

      degree is a hyperparameter. You need try different degrees to find out the best fit for you. Thank you for the question. May be someone will benefit from it.

  • @annajuliaschwarz490
    @annajuliaschwarz490 Před 25 dny

    why did u choose degree 6?

    • @regenerativetoday4244
      @regenerativetoday4244  Před 24 dny

      That's just an estimate. degree is a hyperparameter here that you need to try different values to find the right one for you. Look at this video where you will find a method to tune the hyperparameter faster: czcams.com/video/km71sruT9jE/video.html