Parametric vs Non Parametric Machine Learning | Difference between Parametric and Non Parametric ML

Parametric vs Non Parametric Machine Learning | Difference between Parametric and Non Parametric ML
#ParametricVsNonParametricMachineLearning #UnfoldDataScience
Hello ,
My name is Aman and I am a Data Scientist.
About this video:
In this video, I explain about parametric and non parametric machine learning methods. I explain with example what is the difference between parametric and non parametric machine learning with example. Below topics are explained in this video:
1. Parametric vs Non Parametric Machine Learning
2. Difference between Parametric and Non Parametric ML
3. What is parametric and non parametric machine learning
4. Parametric vs non parametric regression
5. Parametric vs non parametric
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[@fouried96]

High bias: Parametric models
Low bias: Non-parametric models.
Thanks for the video!

[@sanyamjain840]

The basic idea behind the parametric method is that there is a set of fixed parameters that uses to determine a probability model
In non parametric model, there is no fixed set of parameters available, and also there is no distribution (normal distribution, etc.) of any kind is available for use. This is also the reason that nonparametric methods have high accuracy.
Therefore A non-parametric model will always have a higher prediction accuracy compared to a parametric model.

[@UnfoldDataScience]

Yes true, Sanyam.

[@Krishna-pn5je]

Hi Aman,
very nice explanations. please find the below answers.
The parametric models has high bias due to simplified assumptions on the data(i.e. data is linearly separable).Because of high bias we may have underfitted models which high training error and high CV error .
The non-parametric models are overfitted models to the input data. They have low training error and high CV error. when there
is any change in the training data the training error also increases.

[@antonioflores4240]

Very well explained!

[@tempura_edward4330]

I was exactly looking for explanation on this topic and your video answered all my questions! Again, Thank you for your great work!

[@tempura_edward4330]

So parametric models tend to have more bias and non parametric models tend to have less bias but more variance.

[@UnfoldDataScience]

Thank you. Yes. Right answer.

[@ArvindSingh-qc6si]

in non parametric there should be low bias due to overfitting
and in parametric there should be high bias cause of underfitting.

[@libeamlakbekele6345]

Great explanation thank you
Q How is Gaussian process regression non-parametric, I mean it assumes something at first which is the kernel. if we are assuming a prior how can we say something is non-parametric. Can you please explain this

[@bruceWayne19993]

we prefer non parametric models over parametric models for solving our problems. correct me if i am wrong?

[@russellandrady]

Great

[@UnfoldDataScience]

Thank you

[@CoderIRQ]

thanks Aman, very clear explanation

[@UnfoldDataScience]

My pleasure Assad.

[@jayashreehv5222]

hi Aman, very clear explanation, appreciate the effort. Could you please help on statistical parametric and non parametric tests, when to use parametric and when to use non parametric tests

[@AjayKumar-id7mb]

Thanks, Bro More Videos like this

[@UnfoldDataScience]

Hi Ajay.

[@soumikdey1456]

Non parametric- low bias
Parametric - high bias

[@UnfoldDataScience]

Yes

[@kidya-moohustories4764]

thank you... cleared ans is non parametric group will have low bias as the work on population data

[@RamanKumar-ss2ro]

Very good content.

[@UnfoldDataScience]

Much appreciated

[@vcalls9146]

How the new data is handled after the model is moved to production. Example: During model development the categorical data is converted to 1 and 0 using one hot encoding... When the new data is applied in production how the categorical data or text data is processed..

[@UnfoldDataScience]

Very good question, all the preprocessing should happen on new data as well.

[@akibhossain778]

great video!!

[@UnfoldDataScience]

Thank you!!

[@user-ur2en1zq4f]

loved it. thanks

[@someshwarkapgate9796]

thanks Sir, nicely explained

[@UnfoldDataScience]

Welcome Someshwar.

[@arkophredy]

Thanks for this video, I really appreciate it.

[@kirandeepmarala5541]

Hi Sir.Very Crystal Clear..Superly Explained..When Can we Expect Another Mock Interview get Uploaded..Thank you..

[@UnfoldDataScience]

Thanks Kirandeep.

[@syantospeak112]

Nice video sir

[@UnfoldDataScience]

Thank you

[@HealthyFoodBae_]

Thank you- could you please do non parametric regression in Python?
Thank you

[@UnfoldDataScience]

Will try to upload.

[@sandipansarkar9211]

finished watching

[@sarkersunzidmahmud2875]

😍

[@SeppyDawg]

Thank you!!!!

[@UnfoldDataScience]

You're welcome!

[@shubhamsinghrajput218]

Thanks

[@minhazuddin722]

Collar niche rehta to ek decent teacher wali feeling aati video dekhne me. Bt aisa laga as if apna sutta partner samjha raha ho kch technical baatey.

[@sudheeshe1384]

Nice explanation bro

[@UnfoldDataScience]

Thank you 🙂

[@sudheeshe1384]

@@UnfoldDataScience bro please do the video on L1 & L2 regularization

[@sahanipradeep5614]

thank you so much

[@UnfoldDataScience]

Welcome.

[@HealthyFoodBae_]

Also - when you say we need more data for non parametric, could you explain how much data is needed please

[@UnfoldDataScience]

Depends, at least 50k I would say.

[@akshayv6725]

sir , how are all these implemented in real life . could you please explain?

[@akvines6836]

Good but specking speed need to must me increase

[@sandipansarkar9211]

great explanation

[@UnfoldDataScience]

Glad you liked it

[@ShifatHossain-dj5wn]

High Bias: Parametric?
Low Bias: Non parametric?

[@ankurkapuriya5846]

Bhai thoda 2x mai bola karo, subeh exam dene b jana hai

[@abhijeetpatil3447]

Non peremetric data becz giving high data

[@anirbansarkar6306]

Based upon the explanation, I will say, as parametric learning algorithms are provinding low fit models, they will have 'high bias'. As a result, they will perform poor (if compared with non-parametric ML algos) on both train and test data.
On the other hand, as non-parametric algorithms tends to overfit, they might perform well with train data, but on real life data performace may degrade. So this is a case of 'high variance'.
But I have a small doubt, when you said, we assume something about f(x) [and you gave a very nice real world example], what assumptions were you trying to imply? (I mean in terms of dataset, what are those assumtions, that we make on dependent variable of our dataset)

[@UnfoldDataScience]

example like "Salary" is linear function of "experience".

[@leen2298]

Thank you soooo much 🤍🤍✨, i was afraid from my final exam but now I’m not 😌

[@UnfoldDataScience]

You're welcome 😊

[@brianocorner6467]

Pass HOA 😂😂??

[@user-ur2en1zq4f]

loved it. thanks