Log scaling, which is particularly effective for data with non-uniform distributions by compressing the data range through logarithmic transformation.

Scaling to a range, which adjusts feature values to a standardized range, such as 0 to 1 or -1 to +1, suitable for uniformly distributed data.

Clipping, which removes extreme outliers by ensuring data falls within a specified range, thus improving model stability.

Z-score normalization, which measures how many standard deviations a value is from the mean, providing a sense of variability and normalizing the data.

All of the above techniques can be applied depending on the specific characteristics of the data and the model's requirements.