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Why is the R-value 0.95 for raw data but drops to 0.85 for normalized data in ANN?

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Why is the R-value 0.95 for raw data but drops to 0.85 for normalized data in ANN ? does it happen or am I doing something wrong

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akshatsood
akshatsood on 10 Dec 2023
Edited: akshatsood on 10 Dec 2023
I understand that you are observing an R-value of 0.95 for raw data, which drops to 0.85 for normalized data in your Artificial Neural Network (ANN). I would like to clarify whether these values are from the training dataset or the testing dataset or the validation dataset.
In general, the higher R-value associated with raw data might indeed suggest overfitting, potentially stemming from inadequate feature scaling. By normalizing the data, you are likely achieving more effective feature scaling, which can help prevent overfitting. Consequently, the decrease in the R-value for your Artificial Neural Network model when using normalized data could signify a more robust and generalizable model. This shift in R-value may indicate that the model is now better at capturing underlying patterns within the data, leading to improved generalization.
I hope this helps.
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akshatsood
akshatsood on 10 Dec 2023
Edited: akshatsood on 10 Dec 2023
You can eliminate the data points located at or near the origin by using logical indexing. This method involves extracting a specific portion of the data based on defined conditions, effectively filtering out the unwanted points. Additionally, I recommend reviewing the following articles that go deep into identifying and cleaning outliers.
I hope this helps.

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