Questions tagged [standardization]
Usually refers to "z-standardization" which is shifting and rescaling data to assure they have zero mean and unit variance. Other "standardizations" are possible, too.
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When conducting multiple regression, when should you center your predictor variables & when should you standardize them?
In some literature, I have read that a regression with multiple explanatory variables, if in different units, needed to be standardized. (Standardizing consists in subtracting the mean and dividing ...
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Ridge\Lasso -- Standardization of dummy indicators
Say I have a data set with say 5000 rows and about 150 columns (5000 samples, 150 predictors/features) and I'm interested in a applying a ridge or lasso regression. (Let us assume using a logit link ...
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Variables are often adjusted (e.g. standardised) before making a model - when is this a good idea, and when is it a bad one?
In what circumstances would you want to, or not want to scale or standardize a variable prior to model fitting? And what are the advantages / disadvantages of scaling a variable?
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What's the difference between Normalization and Standardization?
At work we were discussing this as my boss has never heard of normalization. In Linear Algebra, Normalization seems to refer to the dividing of a vector by its length. And in statistics, ...
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How to apply standardization/normalization to train- and testset if prediction is the goal?
Do I transform all my data or folds (if CV is applied) at the same time? e.g.
(allData - mean(allData)) / sd(allData)
Do I transform trainset and testset ...
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Data normalization and standardization in neural networks
I am trying to predict the outcome of a complex system using neural networks (ANN's). The outcome (dependent) values range between 0 and 10,000. The different input variables have different ranges. ...
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whether to rescale indicator / binary / dummy predictors for LASSO
For the LASSO (and other model selecting procedures) it is crucial to rescale the predictors. The general recommendation I follow is simply to use a 0 mean, 1 standard deviation normalization for ...
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Does standardising independent variables reduce collinearity?
I've come across a very good text on Bayes/MCMC. IT suggests that a standardisation of your independent variables will make an MCMC (Metropolis) algorithm more efficient, but also that it may reduce (...
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Is standardisation before Lasso really necessary?
I have read three main reasons for standardising variables before something such as Lasso regression:
1) Interpretability of coefficients.
2) Ability to rank the ...
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Perform feature normalization before or within model validation?
A common good practice in Machine Learning is to do feature normalization or data standardization of the predictor variables, that's it, center the data substracting the mean and normalize it dividing ...
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When and how to use standardized explanatory variables in linear regression
I have 2 simple questions about linear regression:
When is it advised to standardize the explanatory variables?
Once estimation is carried out with standardized values, how can one predict with new ...
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Converting standardized betas back to original variables
I realise this is probably a very simple question but after searching I can't find the answer I am looking for.
I have a problem where I need to standardize the variables run the (ridge regression) ...
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Is standardization needed before fitting logistic regression?
My question is do we need to standardize the data set to make sure all variables have the same scale, between [0,1], before fitting logistic regression. The formula is:
$$\frac{x_i-\min(x_i)}{\max(...
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What does "normalization" mean and how to verify that a sample or a distribution is normalized?
I have a question in which it asks to verify whether if the Uniform distribution (${\rm Uniform}(a,b)$) is normalized.
For one, what does it mean for any distribution to be normalized?
And two, how ...
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What's the difference between standardization and studentization?
Is it that in standardization variance is known while in studentization it is not known and therefore estimated?
Thank you.
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