Adjusted R squared explained | Adjusted R squared explained with Python example

[@mukutsharma4078]

Best and easiest explanation on the internet

[@UnfoldDataScience]

Thanks Mukut.

[@dr.walidsoula]

Clear explanation of Adjusted R squared 👏

[@nitinchauhan242]

i watch your video today andi got amazed by your skills how you explain simply thankA lot

[@UnfoldDataScience]

Thanks Nitin for feedback

[@nareshjadhav4962]

Thanks Aman. Much cleared now😊

[@UnfoldDataScience]

Welcome Naresh.

[@souravbiswas6892]

Awesome explanation, hence comes the concept of parsimonious model,- obtaining higher accuracy with fewer no of predictors..

[@anuradhabalasubramanian9845]

Awesome Explanation Aman ! Great job

[@rajalakshmisathivasagam4839]

Nice explanation.

[@rahultivarekar6768]

NIce informative video. Thank you.

[@lancelotdsouza4705]

Thanks for the beautiful videos, love your explanations, so simple but yet profound. Can't thank you enough. Your videos are better than paid courses

[@UnfoldDataScience]

Thanks for watching Lancelot. Your words mean a lot to me.

[@sanketadamapure802]

Great Aman Sir ....!!!🙂

[@UnfoldDataScience]

Thanks For watching Sanket.

[@henrikherrmann540]

Great video

[@upendram2820]

Very good sir..

[@UnfoldDataScience]

Thanks Upendra.

[@sadhnarai8757]

Very good Aman

[@UnfoldDataScience]

Thank you.

[@fahadnasir1605]

Thank you for very detailed video, outstanding work Aman. One questions - When I create model in python with one independent variable, the output shows adjusted R square in it, how will we interpret adjusted R square for a model which has one independent variable?

[@aravinthmegnath3569]

For a model with one independent variable, the adjusted R-squared will be the same as the regular R-squared value. The R-squared value ranges from 0 to 1, with 1 indicating that the model perfectly fits the data, and 0 indicating no fit at all. Therefore, if you have a model with only one independent variable, you can interpret the adjusted R-squared value as the proportion of the variation in the dependent variable that can be explained by that independent variable.

[@viratmani7011]

Based on Rsquared score are we selecting features? Is R squared is a feature selection technique for linear regression?

[@UnfoldDataScience]

In one way we can say yes, if your adjusted R squared is dropping you may want to revisit the variable and it's contribution on model learning.

[@omkarfadtare3054]

Thanks you explain every concept in simple words which are so easy to understand..🙏👍 and Thanks to the recommendation engine as well which recommended me your channel 😄😄

[@UnfoldDataScience]

It's my pleasure

[@dilnawazahmed949]

Kya padhate h bhai aap Maza aa gya 👌👌

[@UnfoldDataScience]

Pls share with friends as well. Thank you.

[@dilnawazahmed949]

@@UnfoldDataScience Okay Sir 👍

[@ankitachaudhari99]

Good crisp explanation

[@UnfoldDataScience]

Thanks Ankita. Please share with friends as well.

[@ankitachaudhari99]

@@UnfoldDataScience yes

[@letslearnwithumesh4360]

You explain very good

[@UnfoldDataScience]

Thanks a lot

[@bhavithakusam7962]

What's the difference between adjusted-r square and feature importance? And when to use them

[@UnfoldDataScience]

Feature importance is at feature level/Column level whereas adjusted R squared is at model level.

[@SACHINKUMAR-px8kq]

Thanks sir

[@rezazaman5278]

Thanks for such excellent videos. Just to make my understanding correct about the formula: R2 = 1 - [(1-R2)*(n-1)/(n-k-1)] Questions : 1. Is Adj. R2 calculated based on train and test dataset independently or on entire dataset ? 2. model.score(X, y) : are X and y from entire dataset or from X_train,/y_train or from X_test/y_test separately ? n = is it the total observations in actual dataset or depends on the length of train and test dataset ? k = X.shape[-1] , X from actual dataset or from X_train/y_train? Thanks Aman.

[@aravinthmegnath3569]

1.The adjusted R-squared is calculated based on the entire dataset used to build the model. It is a measure of how well the model fits the data overall and takes into account the number of independent variables in the model.

[@subhajitdutta1443]

Hello Aman, Can u please help me to know if we add some important feature then what will happen to adjusted R square? Is it going to increase or decrease?

[@UnfoldDataScience]

if its important, it should increase

[@subhajitdutta1443]

@@UnfoldDataScience Thanks for your quick response.

[@subhajitdutta1443]

Epic

[@UnfoldDataScience]

Thanks Subhajit🙂

[@willhallahan3986]

Actually I have heard of research that indeed employee height influences payrates. Taller workers get more $$$$!

[@supriyasingh8083]

How did one land at this formula

[@shiv_at_work]

Happy Teacher's Day

[@UnfoldDataScience]

Thanks Shivani.