Adjusted R squared explained | Adjusted R squared explained with Python example
HTML-код
- Опубликовано: 30 сен 2024
- Adjusted R squared explained | Adjusted R squared explained with Python example
#AdjustedRSquared #UnfoldDataScience
Hello ,
My name is Aman and I am a Data Scientist.
About this video:
In this video, I explain about Adjusted R squared with a python example and mathematics behind it. Below topics are discussed in this video:
1. Adjusted R squared explained
2. Adjusted R squared explained with Python example
3. Adjusted R squared demo
4. R squared vs Adjusted R squared
5. Adjusted R squared example
About Unfold Data science: This channel is to help people understand basics of data science through simple examples in easy way. Anybody without having prior knowledge of computer programming or statistics or machine learning and artificial intelligence can get an understanding of data science at high level through this channel. The videos uploaded will not be very technical in nature and hence it can be easily grasped by viewers from different background as well.
If you need Data Science training from scratch . Please fill this form (Please Note: Training is chargeable)
docs.google.co...
Book recommendation for Data Science:
Category 1 - Must Read For Every Data Scientist:
The Elements of Statistical Learning by Trevor Hastie - amzn.to/37wMo9H
Python Data Science Handbook - amzn.to/31UCScm
Business Statistics By Ken Black - amzn.to/2LObAA5
Hands-On Machine Learning with Scikit Learn, Keras, and TensorFlow by Aurelien Geron - amzn.to/3gV8sO9
Ctaegory 2 - Overall Data Science:
The Art of Data Science By Roger D. Peng - amzn.to/2KD75aD
Predictive Analytics By By Eric Siegel - amzn.to/3nsQftV
Data Science for Business By Foster Provost - amzn.to/3ajN8QZ
Category 3 - Statistics and Mathematics:
Naked Statistics By Charles Wheelan - amzn.to/3gXLdmp
Practical Statistics for Data Scientist By Peter Bruce - amzn.to/37wL9Y5
Category 4 - Machine Learning:
Introduction to machine learning by Andreas C Muller - amzn.to/3oZ3X7T
The Hundred Page Machine Learning Book by Andriy Burkov - amzn.to/3pdqCxJ
Category 5 - Programming:
The Pragmatic Programmer by David Thomas - amzn.to/2WqWXVj
Clean Code by Robert C. Martin - amzn.to/3oYOdlt
My Studio Setup:
My Camera : amzn.to/3mwXI9I
My Mic : amzn.to/34phfD0
My Tripod : amzn.to/3r4HeJA
My Ring Light : amzn.to/3gZz00F
Join Facebook group :
www.facebook.c...
Follow on medium : / amanrai77
Follow on quora: www.quora.com/...
Follow on twitter : @unfoldds
Get connected on LinkedIn : / aman-kumar-b4881440
Follow on Instagram : unfolddatascience
Watch Introduction to Data Science full playlist here : • Data Science In 15 Min...
Watch python for data science playlist here:
• Python Basics For Data...
Watch statistics and mathematics playlist here :
• Measures of Central Te...
Watch End to End Implementation of a simple machine learning model in Python here:
• How Does Machine Learn...
Learn Ensemble Model, Bagging and Boosting here:
• Introduction to Ensemb...
Build Career in Data Science Playlist:
• Channel updates - Unfo...
Artificial Neural Network and Deep Learning Playlist:
• Intuition behind neura...
Natural langugae Processing playlist:
• Natural Language Proce...
Understanding and building recommendation system:
• Recommendation System ...
Access all my codes here:
drive.google.c...
Have a different question for me? Ask me here : docs.google.co...
My Music: www.bensound.c...
Actually I have heard of research that indeed employee height influences payrates. Taller workers get more $$$$!
Thanks Aman. Much cleared now😊
Welcome Naresh.
Nice explanation.
How did one land at this formula
What's the difference between adjusted-r square and feature importance? And when to use them
Feature importance is at feature level/Column level whereas adjusted R squared is at model level.
Based on Rsquared score are we selecting features? Is R squared is a feature selection technique for linear regression?
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.
Awesome explanation, hence comes the concept of parsimonious model,- obtaining higher accuracy with fewer no of predictors..
Clear explanation of Adjusted R squared 👏
NIce informative video. Thank you.
Best and easiest explanation on the internet
Thanks Mukut.
Awesome Explanation Aman ! Great job
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?
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.
You explain very good
Thanks a lot
Great Aman Sir ....!!!🙂
Thanks For watching Sanket.
Very good Aman
Thank you.
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.
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.
Happy Teacher's Day
Thanks Shivani.
Very good sir..
Thanks Upendra.
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
Thanks for watching Lancelot. Your words mean a lot to me.
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 😄😄
It's my pleasure
i watch your video today andi got amazed by your skills how you explain simply thankA lot
Thanks Nitin for feedback
Good crisp explanation
Thanks Ankita. Please share with friends as well.
@@UnfoldDataScience yes
Epic
Thanks Subhajit🙂
Great video
Thanks sir
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?
if its important, it should increase
@@UnfoldDataScience Thanks for your quick response.
Kya padhate h bhai aap
Maza aa gya 👌👌
Pls share with friends as well. Thank you.
@@UnfoldDataScience Okay Sir 👍