normalization - Why do we need to normalize data before …
I'm doing principal component analysis on my dataset and my professor told me that I should normalize the data before doing the analysis. Why? What would happen If I did PCA without …
Normalizing data for better interpretation of results?
Jul 13, 2021 · 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. …
standard deviation - "normalizing" std dev? - Cross Validated
Jun 26, 2015 · 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 things …
The correct way to normalize time series data - Cross Validated
Feb 7, 2018 · Yes, indeed, regarding: "Finally, in both cases I believe I should compute Xi and S (or Xi (t) and S (t)) based only on training set data, and use the values so computed to …
when should I normalize with $\\log(1+x)$ instead of with $\\log$?
Nov 8, 2019 · 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 …
Why normalize images by subtracting dataset's image mean, …
May 8, 2016 · Consistency: Normalizing with the dataset mean ensures all images are treated the same, providing a stable input distribution. Preserves Important Features: Keeps global …
hypothesis testing - Normalization to control - Cross Validated
The obvious (and often used) solution would be to divide each value in the experimental group by the mean of the corresponding control group (I have seen this described as "calculating the …
When to normalize data in regression? - Cross Validated
Mar 16, 2016 · Under what circumstances should the data be normalized/standardized when building a regression model. When i asked this question to a stats major, he gave me an …
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., …
Is it a good practice to always scale/normalize data for machine ...
Jan 7, 2016 · 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? …