Previously, I encountered errors while attempting cross-validation. Today, I successfully addressed those issues. I conducted cross-validation on two distinct datasets: one representing rural areas and the other urban areas with kfold=5. In both datasets, the dependent variable was “Diagnosed diabetes,” and the independent variables included “obesity,” “inactivity,” and “food insecurity.”
For the rural dataset, the results were as follows:
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- Mean Squared Error: 1.54
- Standard Deviation of MSE: 0.17
The model’s predictions, on average, deviate from the actual values by an MSE of 1.54. The standard deviation of 0.17 suggests some variability in prediction accuracy across different cross-validation folds.
For the urban dataset, the results were as follows:
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- Mean Squared Error: 1.12
- Standard Deviation of MSE: 0.10
The model’s predictions have a lower MSE of 1.12 on average compared to the rural dataset. Additionally, the standard deviation of 0.10 indicates relatively consistent prediction performance across different folds.