Wonderful explanation... keep doing what you are doing.
@achendvankar4 ай бұрын
Excellent video. Wonderfully explained. What I gathered from it is as follows: 1. Whenever you make an assumption of the function of your data, then it is a parametric ML algorithm (Linear Regression). However, if you do not make any assumptions about your function, then it is a non-parametric ML algorithm. So if you try to find out y=f(x), then it is parametric ML algorithm. 2. If the number of parameters does not grow with respect to the number of rows present in your data, then it is a parametric algorithm. However, if the number of parameters grow with respect to the number of rows present in your data, then it is a non-parametric algorithm. 3. It is incorrect to assume that non-parametric algorithms do not have parameters. It is just that they change or rather grow with the number of rows in your data.
@ashwinidikonda21462 жыл бұрын
Your explanation is amazing sir Please continue this playlist
@technicaljp8151 Жыл бұрын
Thank you sir provided by some knowledge
@77sayak Жыл бұрын
Thanks for such a simplistic explanation ❤
@geekyprogrammer48312 жыл бұрын
Please continue tutorials in English. You explained it flawlessly!!
@saptarshisanyal67382 жыл бұрын
Great great explanation
@ammujacob6508 Жыл бұрын
Amazing explanation. Thank you so much.... Linear regression is parametric then what about multiple linear regression?
@MonikaSingh-nu5sg2 жыл бұрын
best explanation 👍
@nikitasinha81817 ай бұрын
Bestest explanation
@Neerajsingh-th3kc2 жыл бұрын
sir, when you are going to launch other question answers
@vedprakashyadav13347 ай бұрын
great explaination sir
@arvindhh37202 жыл бұрын
Super Explaination
@shubhamtelang91272 жыл бұрын
Thank you sir for this playlist
@ShubhamSharma-gs9pt2 жыл бұрын
thanks for the great explanation!!
@marcusmitchelle1488 Жыл бұрын
what are parameters for decision trees? just like slope and interceptor for linear regression.
@lol-ki5pd4 ай бұрын
Parameter are the The Nodes or split point of each feature. take example of cgpa and job_selection: If node is cgpa and it says if cgpa is greator than 5 , you will get job else you will not so on point 5 , consider a line below which all failed and obove which all passed. This is line, again when we go to higher dimension, line becomes plane and more