Overfitting and Underfitting explained with Examples

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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.

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  1. Code With Aarohi
  2. Overfitting
  3. Underfitting
  4. Problems with machine learning
  5. Overfitting with Practical Example
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CodeWithAarohi
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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

MdOsman-tjdr
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Hi! What about the fitting model in term of accuracy differences? what should the acceptable difference between training data and test data in percentage ?

patientmuke
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Thanks to upload.it helping in my work👍🏻

gaganmalhi
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Thankyou!
Can you provide the link to the code?

nehalverma
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can you provide the data sets for fruit dataset increased????

piyushborhade
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can you provide the data sets for fruit dataset increased????

piyushborhade