How to optimize the parameters to make the RMSE fitting value close to 0 when predicting multiple output items through multiple input items? #5445
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What about the training loss? If it's small, it will be overfitting. Otherwise, |
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Hello Maintainer. |
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I have found the reason why the RMSE value cannot be reduced to 1. x. Because the calculation of RMSE is sensitive to 0 values, it is necessary to process the 0 values in “y_true” and "y_pred" before the calculation. |
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The following is the practical process
1:The experimental data for the original concrete composition ratio is 1000 pieces, and the algorithm model used is the random forest regression model.
2:The following code is for creating models, training models, predicting target values, and optimizing Optuna.
The fitting index of RMSE is 27.781625571862275.
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