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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What type of Normalisation technique is best for image data before applying any CNN deep learning model on type of it? [duplicate]

How to decide upon the normalisation that need to be used for image classification or cv problems , Is there any standard on what to choose when in particularly with Image data ?
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Feature Scaling/Standardization or Change Point Score?

I've different data sets that have the feature Volume. This feature represents the absolute number of events. Each observation represents a period (a fixed period such as 15 minutes). You can imagine ...
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CFA: scaling of measured variables/indicators

I'm running a latent variable analysis with: 166 observations 21 continuous variables using the R package lavaan A simple run of ...
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61 views

Two dumb questions about standardization and overfitting [closed]

The following two questions may seem to be dumb, but I could not figure out reasons to convince myself. Question 1 Why neural networks (or more generally, any machine learning models) tend to ...
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Effect of learning rate (and standardization) on NN with ReLu layers [duplicate]

I'm trying to understand the effect of the learning rate on a 10x10x10x10x4 sequential NN. Where each hidden layer is ReLu and the output layer being Softmax. I know the theory: low rate -> slow ...
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31 views

Significance for regression or standardized regression coef

I'm calculating multiple regression with R and trying to decide which predictors to keep and which to drop. I realized that when I use the lm.beta function I'm not ...
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Why would you subtract the mean of your variance from your variance? [duplicate]

i recently stumbled over the following codeline: variancedm<-variance-mean(variance) Is this a common way to normalize/standardize variance ? Why would you ...
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59 views

Do we need to standardize when our data is univariate?

In this question: What algorithms need feature scaling, beside from SVM? it is said that we need to standardize so that all features are weighted equally. But what if we only have as features: time ...
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Interpretation of a regression coefficient

How should I interpret my regression result as my independent variable is in log format and my dependent variable is standardized to a mean of zero and standard deviation of one. More specifically, is ...
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Should you standardize your variables before or after removing outliers?

Barring the question of how to operationalize outliers, or the utility of doing so, and assuming dependent variables and independent variables are all scaled in the main regression specification (...
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transform different measures for correlation analysis

I inherited data from different companies which used different tools to measure customer preference. Now I would like to correlate customer preference with another variable. The problem is that ...
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Which Comes First: Standardization or Transformation?

I have a data frame that contains a few variables where the skew is larger than 1. Also most of the variables have vastly different scales. I am looking to scale the data using R's scale() function (...
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Help needed to Interpret ln(y) = a +b (Standardized X)

I am analysing server data and I have a scenario where I need to get the % by which Y is changed because of a unit change in X: EDIT: I am doing a Linear Regression in Python (and its other forms ...
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Difference between standardizing variables and using Mahalanobis distance

I am wondering how and/or why the Mahalanobis distance is different from using the Euclidean distance on standardized variables?
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In linear regression, what would it do to center the label?

In this question linked below, it was addressed why we would center the features in linear regression. When conducting multiple regression, when should you center your predictor variables & when ...
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Standardizing dummy variable in multiple linear regression?

I have a multiple linear regression model with several independent variables in different units. Because some of my data is negative, I am unable to take the log and therefore am standardizing the ...
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How to (and if) transform and standardize two types of data (count proportions & low-value inflated latencies) for a MRIM model

I am looking to analyse a variety of traits in a Multivariate random intercept model (MRIM) with the help of the MCMCglmm package in R. All traits are measured on different scales so I wish to ...
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Standardizing variables in lavaan (cross-lagged panel model)

I am running cross-lagged panel models with lavaan (3 time points, with and without random intercepts, as shown here https://jflournoy.github.io/2017/10/20/riclpm-lavaan-demo/ ). I noticed that when I ...
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Normalizing data before or after extracting time domain features

I have 100 time series (with 200 instances each) datasets each corresponding to a particular activity. I want to perform supervised classification for the activity. I want to use time domain (time-...
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Interactions: mean centering, standardizing and standardized coefficients (betas)

I mean-center my independent and moderator variable before calculating the interaction term to avoid multicollinearity. In my regression output table, I subsequently report the standardized ...
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Comparing z-scores, one variable pre-/post test

I am working on an assignment where I am going to compare a group of students test scores measured in a pre-test with the same group of students test scores measured in a post test. Due to the way ...
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Can I standardize/normalize proportion data using z-scores

I am trying to determine the temporal repeatability (repeatability over time) by conducting the same test on individuals twice. I then try to compare the results from these tests. I have a variety of ...
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Data normalization in ridge regression when there is no intercept

I would like to have a linear model without an intercept and also without the target being centered. How should my data then be normalized when using ridge regression? If I standardized the variables ...
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High Correlation after Standardization

I was working on a time series data, where there's a very low relationship between the variables(0.1 to -0.1). After applying standardization to each of the features, half of the it starts to bear ...
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How to interpret impulse response analysis in VAR when using standardized variables?

How to interpret impulse response analysis when using standardized variables (ie., subtracting the mean and divide by standard deviation) in vector autoregression analysis? The reason why I ...
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Quadratic term of standardized predictor in logistic regression

A random intercept logistic regression is performed to assess the association between $Y$: Disease (Yes/No) and Standardized Predictor($X_1$) adjusting for control variables ($X_2$, $X_3$) based on ...
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Why does AIC model rank order change in lme models with standardization of predictor variables?

I can't figure this out. The AIC/AICc rank of my mixed effect models are different whether or not I standardize my predictor values using rescale. Note, I'm not concerned that AICc is changing, as ...
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Do I scale panel data as a whole or do I group it first?

I am running a panel regression estimating the effect of a change in employee satisfaction for a given company on the stock price(adjusted by Fama-French). I do have a panel with 50 companies and 43 ...
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Confused about z-score image normalization output

I am trying to normalize my input data for a convolutional network, I applied the z-score normalization technique to my image dataset as follows: Formula: (image - mean(image)) / std(image) ...
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Chance&ceiling performance when using non-standardised hit&false-alarm rates

What is lost/missed out on if defining d', the sensitivity index from Signal Detection Theory, based on non-standardised rates? For example, Patel et al. 2008, for a task where normal and anomalous ...
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Interpretation of standardized (z-score rescaled) linear model coefficients

I have analyzed some data on vegetation change as a function of change in soil parameters. I compared a dataset from 2001 with a dataset from 2018 (fully balanced). To investigate the change in ...
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Standardizing versus percentile rank with survey data

I am currently working with survey data, collected across different locations, but with slightly different units of measurement. For example, on questions such as "What is your level of education", ...
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What is the proper way to standardize non-stationary data?

I have a 19-year time series of satellite imagery (spaced irregularly temporally). The mean and standard deviation of the dataset changes over the 19 years. I get multiple variables from each image; ...
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Variable selection with standardised variables

Recently I performed a lasso regression on a set of 1000 standardised time series variables to select variables to use in a linear regression model. I used the non-standardised original form of the ...
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How to standardize data with low variance?

I have quarterly data of federal fund rate (test set), e.g.: ...
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Standardization based on subsamples

I need to analyze data on the cognitive performance in a sample of participants divided into clinical (36 participants) and non-clinical groups (~100 participants). What would be the correct ...
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Standardised mean absolute error (SMAE) and how to calculate it?

I am using the mean absolute error mean(abs(obs - pred)) as one of the measures assessing the fit of my model. I would also like to have a standardised measure ...
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Z score standardisation vs min-max scaling for feature selection

I am applying l1 norm on the input weights of a single layer MLP. I wanted to know if I should standardize or min-max scale ([0 1] feature scaling) my input data?
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Construct least-squares ultrametric (hierarchical clustering) fits to doubly-standardized “flow” tables and compare to single-linkage-type fits

Figure 1 of the paper, "Hierarchical Migration Regions of France" (IEEE Transactions on Systems, Man and Cybernetics, 4 (1976) 321-324) (https://www.researchgate.net/publication/...
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Standardization of Data

I have a dataset which consists of Sales for Product1 and Product2. It also tells if the <...
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Direct standardisation with missing values on ages

I am aiming to perform a direct standardisation to calculate the prevalence rate of a certain condition in an area. What I did was to merge different routinely collected health data to be able to ...
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Calculating ICC using z-scored standardized or unstandardized DV in Multilevel Linear Model?

I am doing multilevel linear modelling and I am calculating my ICC for my random intercept model. However, when I use the z-scored standardized DV (reaction time), the intercept and residual variance ...
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Should I standardize all variables before a PCA separately if some share the same units

I have a matrix that contains >2000 variables which can be divided in 4 groups of ~500 variables with each group having a distinct unit. I need to standardize the matrix before running a PCA, but when ...
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Group mean&SD with respect to which z-scores are computed

I have a research report that gives the standardised test scores from a number of subjects. However, it is not specified with respect to which group the z-scoring was made. Thus, for each subject ...
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Questions about standard error

We are trying to create a table of standardised effects and standardised errors to compute for a meta-analysis. And I had a few questions around this Can you get a standard error for pearson ...
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Un-standardize feature weights

I have a linear regression model $y_a = \theta_a^T\tilde{f}$, where $\theta_a$ is a vector of learned feature weights and $\tilde{f}$ is my standardised feature vector; $$ \tilde{f} = \frac{f - \mu}{\...
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Why z-score/standard score is a linear transformation

Can someone please help me understand why the standard score $(X - \mu)/\sigma$ is a linear transformation since both mean and standard deviation depend on X?
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Centering / standardizing leads to very different results for GLM (logistic, poisson, negative binomial distribution)

I have a dataset with count data and around 1 million observations. My regressions contain around 40 variables (binary and continuous) and 10 thousand fixed effects. I analyze this dataset with linear,...
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What is the reasoning behind standardization (dividing by standard deviation)?

Why does dividing a dataset by sigma make the sample variance equal to 1? Assuming a zero mean for simplicity. What's the intuition behind this? Dividing by the range (max-min) makes intuitive sense....
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MIMIC Model and standardization

I'm running a MIMIC model in MPlus with a dummy coded covariates and binary manifest variables. Which standardization should I use to calculate the ETS effect size for DIF, std, stdy, or stdyx?