Hi @영훈 정
Yes, standardizing your data is a good idea when using Gaussian regression because it can improve the performance of the model by making the data more comparable across different features.
Here are a few methods you can use to standardize your data:
- Z-score normalization: This is a commonly used method that scales the data to have a mean of 0 and a standard deviation of 1. This can be achieved by subtracting the mean of each feature and dividing by its standard deviation.
- Min-Max scaling: This method scales the data to a fixed range between 0 and 1. This can be achieved by subtracting the minimum value of each feature and dividing by the range of the feature.
- Log transformation: This method can be used when the data is skewed or has a wide range of values. By taking the logarithm of the data, the values can be scaled down and brought closer together.
- Robust scaling: This method is similar to Z-score normalization, but instead of using the standard deviation, it uses the interquartile range to scale the data. This can be useful when the data contains outliers.
You can use any of these methods to standardize both your training data and new data for prediction. Just make sure that you apply the same method and parameters to both the training and new data to ensure consistency.
