Overfitting and Underfitting explained with Examples
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- Опубликовано: 11 сен 2024
- Explained Overfitting and Underfitting in a simpler form (Theoretically and practically).
The reason of poor performance of any algorithm in machine learning is either overfitting or underfitting the data.
What is Overfitting : When algorithm perform well on training data but performance is bad on test data.
What is Underfitting : Perform poor on training and test data.
If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer your queries.
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You are a good teacher, thank you
Glad you think so!
like the practical way of teaching
Thank you!
Hello There
Thank you for the input. However, as you add more data in oversampling issue, I want to ask what is the other option apart from adding data.Coz in real world, I don't think we will be permissible to add more data in real time projects.
please reply, looking forward for your answer. Thank you
Hi! What about the fitting model in term of accuracy differences? what should the acceptable difference between training data and test data in percentage ?
Very nice video
Thanks
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thank you i like it
I'm glad you like it
Thanks to upload.it helping in my work👍🏻
Most welcome 😊
AWESOME
Thanks 🤗
wow lovely Aarohi
Thank you!
Thankyou!
Can you provide the link to the code?
can you provide the data sets for fruit dataset increased????
github.com/saanyalasod/Fruits/blob/main/fruit%20data.csv
good
Thanks
can you provide the data sets?
github.com/AarohiSingla/Aarohi/blob/master/fruit_dataset.csv
@@CodeWithAarohi thanks
can you provide the data sets for fruit dataset increased????
github.com/saanyalasod/Fruits/blob/main/fruit%20data.csv