when should I normalize with $\\log(1+x)$ instead of with $\\log$?
2019年11月8日 · I've seen people log-normalize data by using the log(1 + x) log (1 + x) (np.log1p) method for instance normalizing the price of diamonds in the diamonds dataset using log1p if …
Normalizing data for better interpretation of results?
2021年7月13日 · Fold-change (or percentage change) is a perfectly reasonable way to want to interpret data, but indeed, just normalizing as you have done creates the issue you've noticed. …
How to normalize data between -1 and 1? - Cross Validated
2015年10月26日 · I have seen the min-max normalization formula but that normalizes values between 0 and 1. How would I normalize my data between -1 and 1? I have both negative and …
Is it a good practice to always scale/normalize data for machine ...
2016年1月7日 · First things first, I don't think there are many questions of the form "Is it a good practice to always X in machine learning" where the answer is going to be definitive. Always? …
Why normalize images by subtracting dataset's image mean, …
2016年5月8日 · Consistency: Normalizing with the dataset mean ensures all images are treated the same, providing a stable input distribution. Preserves Important Features: Keeps global …
Definition of normalized Euclidean distance - Cross Validated
2015年2月4日 · Recently I have started looking for the definition of normalized Euclidean distance between two real vectors $u$ and $v$. So far, I have discovered two apparently ...
The correct way to normalize time series data - Cross Validated
2018年2月7日 · A question very similar to mine has been asked already: Zero mean unit variance normalization of multivariate time series I'm asking a new question because that one didn't …
maximum likelihood - Normalizing flow training - Cross Validated
2021年4月10日 · maximum-likelihood generative-models normalizing-flow Share Cite Improve this question
standard deviation - "normalizing" std dev? - Cross Validated
2015年6月26日 · First of all, I'm not a statistics person but came across this site and figured I'd ask a potentially dumb question: I'm looking at some P&L data where the line items are …
Data normalization and standardization in neural networks
1- Min-max normalization retains the original distribution of scores except for a scaling factor and transforms all the scores into a common range [0, 1]. However, this method is not robust (i.e., …