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This repository has been archived by the owner on Jan 13, 2023. It is now read-only.
Hello, I am studying this book. Thank you for writing such a well-structured textbook.
I have a question about the Bridged Schema pattern in section 23. How should I determine the amount of old data to be added to the training data.
In this repository, it is stated that adding 60,000 old data is best. However, in the line graph of the number of data and the R2 value, 60,000 is rather at the bottom of the R2 value. The higher the R2 value, the better the prediction, so it looks rather like the prediction accuracy is decreasing as older data is added.
From the results of this graph, I may conclude that the prediction accuracy is higher when learning only with new data.
I would be glad if anyone tell me why you decide that adding 60,000 old data was the best.
The text was updated successfully, but these errors were encountered:
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Hello, I am studying this book. Thank you for writing such a well-structured textbook.
I have a question about the Bridged Schema pattern in section 23. How should I determine the amount of old data to be added to the training data.
In this repository, it is stated that adding 60,000 old data is best. However, in the line graph of the number of data and the R2 value, 60,000 is rather at the bottom of the R2 value. The higher the R2 value, the better the prediction, so it looks rather like the prediction accuracy is decreasing as older data is added.
From the results of this graph, I may conclude that the prediction accuracy is higher when learning only with new data.
I would be glad if anyone tell me why you decide that adding 60,000 old data was the best.
The text was updated successfully, but these errors were encountered: