Bias Variance Simplified |Machine Learning Fundamentals: Bias and Variance | Bias Variance Trade off

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  • Опубліковано 23 гру 2024

КОМЕНТАРІ • 74

  • @AshwiniPeacefulEscapes
    @AshwiniPeacefulEscapes 2 роки тому +1

    Finally...You are the only person who made me understand bias and variance all thanks to you..

  • @syedfayeqjeelani54
    @syedfayeqjeelani54 3 роки тому +1

    Aman, thank you man for these small and eloquent topics. You are a savior. Stay blessed.

  • @shikhar_anand
    @shikhar_anand 3 роки тому +1

    Thank you very much Aman for that last point. I actually forgot everything about bias-variance in my interview but not anymore with that last point of explaination. Thanks for keeping it simple..

  • @YogeshKumar-ck2ni
    @YogeshKumar-ck2ni 3 роки тому

    are sir aap ne to kamal kr dia sb kuch samajm m aagya ek baar m thanks you so much for the great lecture

  • @mohdsajidkhan9713
    @mohdsajidkhan9713 3 роки тому

    This is right break through to understand this concept. I will go through video with n number of time. Until or unless. everything fits in my mind.

  • @ArvindKumar-ml6rb
    @ArvindKumar-ml6rb 4 роки тому +1

    Sir.. Ur method of teaching is awesome..

  • @storiesshubham4145
    @storiesshubham4145 Рік тому

    Unbelievably superb explanation!

  • @Sagar_Tachtode_777
    @Sagar_Tachtode_777 4 роки тому +2

    Conclusion:- too much Love for training Data ---} High variance model (decision tree etc) 👌

  • @arshveersingh6499
    @arshveersingh6499 2 роки тому

    Thanks Aman Sir, its very nice explanation given by you in this small video .Your video contain all points which are necessary. Making notes with help of your video became very easy for me. That last point about Bias Variance Trade off is awesome 👏.

  • @oompster
    @oompster Рік тому

    Thank you very much for your video. helping me a lot for this topic

  • @user-cb7it9cv9l
    @user-cb7it9cv9l 3 роки тому

    simple and clear explanation .Thank you for the video

  • @Vinit_Gambhir
    @Vinit_Gambhir 9 місяців тому

    You are a life saver

  • @Sagar_Tachtode_777
    @Sagar_Tachtode_777 4 роки тому +1

    So wonderful explanation. Thanks a lot 🌺🌼🌺

  • @Aniqahns
    @Aniqahns 2 роки тому

    Thank you so much for the last point.

  • @carryminatifan9928
    @carryminatifan9928 4 роки тому +1

    This one is very good explanation. I totally understand the concept ♥️

  • @pawanchadhokar8134
    @pawanchadhokar8134 2 роки тому

    Thanks sir for sharing knowledgeable information

  • @kousikdutta5889
    @kousikdutta5889 2 роки тому

    I have been watching your videos sir , your explanations are wonderful.

  • @surendrareddy7190
    @surendrareddy7190 4 роки тому

    Way of explanation is superb , everyone can understand easily , excellent work sir🙏

  • @charanramtejkodi9220
    @charanramtejkodi9220 3 роки тому +1

    Well explained sir

  • @tusharbedse9523
    @tusharbedse9523 3 роки тому +1

    i like the part "love for training data' :)

  • @bangarrajumuppidu8354
    @bangarrajumuppidu8354 3 роки тому +1

    superb explanation sir!!

  • @firstkaransingh
    @firstkaransingh 2 роки тому

    Excellent explanation 👌

  • @VinodRS01
    @VinodRS01 4 роки тому +1

    Simply awesome thank you so much sir.

    • @UnfoldDataScience
      @UnfoldDataScience  4 роки тому

      Welcome Vinod.

    • @VinodRS01
      @VinodRS01 4 роки тому

      @@UnfoldDataScience sir please make video related to central limit theorem ,

  • @KrishnaKumar-ni8fm
    @KrishnaKumar-ni8fm 4 роки тому +1

    Very useful and nicely explained.

  • @RamanKumar-ss2ro
    @RamanKumar-ss2ro 4 роки тому +1

    Good one again.

  • @carryminatifan9928
    @carryminatifan9928 4 роки тому +2

    Sir how we will know that my model is biased or varianced ?
    Any practical video is there on your channel or any plan to create one?

    • @UnfoldDataScience
      @UnfoldDataScience  4 роки тому +2

      You will get to know on basic of two things first is, how that model fits internally to data and second how the model is performing on unseen data. For ex - Test data.

    • @surendrareddy7190
      @surendrareddy7190 4 роки тому +1

      When your model gives high accuracy on train data and low accuracy on new data(test data) that means our model has high variance .
      Both the time it gives low accuracy means our model has high bias .
      I'm I right sir.🙏

    • @carryminatifan9928
      @carryminatifan9928 4 роки тому

      Thankyou for the reply guys 👍
      Now getting clear.

  • @meenakshirao4990
    @meenakshirao4990 3 роки тому

    Thank you for nice explanation in simple manner

  • @tusharbedse9523
    @tusharbedse9523 3 роки тому +1

    good video

  • @firefly2008god
    @firefly2008god 3 роки тому +1

    Very good explanation. Please make a video on Ridge and Lasso regularisation.

    • @UnfoldDataScience
      @UnfoldDataScience  3 роки тому +1

      Thanks Rahul, video is already there. Please search for "topic name + channel name" I'm UA-cam.

  • @nishikantanayak7797
    @nishikantanayak7797 2 місяці тому

    thank you sir

  • @bharathy516
    @bharathy516 3 роки тому

    Well explained

  • @sandipansarkar9211
    @sandipansarkar9211 3 роки тому

    finished watching

  • @secureblob4874
    @secureblob4874 3 роки тому

    I am new to data science and trying to learn fundamentals. Your explanation is simply superb. I was trying to find out why we use the term bias and finally your video gave me the answer! As per the video, if there is an under lying assumption about the form of the function, it is called bias. So, are you saying that the assumption was a human error? ie, the machine learning engineer made an assumption that he can use liner regression to fit the data? or could it be a problem with the data itself? for example, the sample of data the ML engineer got appears like it can be fitted with a liner regression and then the engineer used Liner regression. But actually the sample data cud be wrong and it gave a wrong impression that it cud be fitted with a linear regression model, but actually the data set about the population is not really a linear regression. So as the sample was wrong, the engineer chose linear regression model and he saw the error when the model is tested against the test data. Can you explain? By the way i subscribed to your channel and i believe your channel would need more subscribers. Keep up the good work.

    • @UnfoldDataScience
      @UnfoldDataScience  3 роки тому

      Thanks for asking.
      The answer is, nature of data depends on type of business we are talking about. Data may totally be different for a tourism company as compared to Reatil.
      Normally, we do not know about data without digging deeper in it however regression models are good to to start with as it is an old and trusted technique.

  • @ashishmehra5143
    @ashishmehra5143 Рік тому

    BIAS NOTES:
    When the form of the function is biased.
    What is the function:
    For example, the linear regression equation is y = mx+c. In other words, we can say the mx + c is a f(x).
    i.e f(x) = mx + c
    What is the form: If we plot the equation, it wil be a line. In this case, the line is the form. (Side note: Form will depend on the function. It could be parabola, cube and so on, it will depend on what function are we plotting.)
    Bias Summary: Coming back to the "bias" using the linear regression function f(x), here the underlying assumption is that the "form" will be a straight line, that is what is called the "bias" of the function.
    In the other words, since line (form) drawn from the function f(x) = mx + c is biased, we can say that model build using the same function is "High Biased Model".

    • @UnfoldDataScience
      @UnfoldDataScience  Рік тому

      Thanks for writing in. Its very important to see the efforts coming from students. Cheers

  • @sheruloves9190
    @sheruloves9190 3 роки тому +1

    I have high variance towards unfold data science

  • @dany28g61
    @dany28g61 3 роки тому

    Clean and crisp superb.. Would you give trainings as well on DS ML DL?

  • @lavanyagowda8186
    @lavanyagowda8186 3 роки тому

    Please update interview questions on data science

  • @abhinavkale4632
    @abhinavkale4632 3 роки тому +2

    The dislikes are from all the edu-tech companies

    • @UnfoldDataScience
      @UnfoldDataScience  3 роки тому

      😃😃kya kare, unko free education acha nahi lagta.

  • @virozz1024
    @virozz1024 3 роки тому

    Y = ax + b , b = bias