Questions tagged [normalization]

Usually "normalization" means re-expressing univariate data to make values lie within a specified range.

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Rescaling the ormalized weight of a variable greater than 1

In my data set, I'm trying to calculate the normalized weight of my cumulative frequency variable. To do this, I divide the cumulative frequency (subset by different unit for every week 't') by the ...
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Feature preprocessing (standardize and normalize) and variable independence

I can't find clarity on this question so here goes: Suppose I have 3 features, x, y, z. I know x and ...
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Hierarchical clustering output 99% belongs to a cluster group

I've just done a clustering analysis using hierarchical clustering analysis in Python but the result is not what I expect. Most of them (483/485) belongs to group 1 and the rest to group 0. Is there ...
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Normalize a Dataset with Min/Max formula [closed]

I have 7 groups of data (let's say g0 all the way to g6) I want to normalize g0, and I apply the following function only to the elements of g0: ...
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Standardization in Machine Learning Models

Please answer this question in two contexts: Context 1 - Performance: Which models are sensitive AND which models are insensitive to standardization? Why? Are there any edge-cases in which ...
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Should I normalize (preprocess/scale/log-transform) my data "before" imputing missing values with missRanger?

I am trying to impute missing values using missRanger package. missRanger is apparently much faster than missForest. I would like to know: 1 - Are these two packages any different in their imputation ...
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t- test for non normally distributed sample

I am doing a statistical test (analysis) for the following case: As part of a product aimed at improving the quality and speed of code writing for developers, we have implemented a new feature that ...
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Parametric or non-parametric if a variable is distributed normally and another non-normally distributed

I have 2 variables that I'd like to know if one (a concentration of a substance in a biological fluid) is dependent on the other (the percentage of inhibition of this fluid over the substance). I'd ...
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The proper name of just subtracting the mean?

https://en.wikipedia.org/wiki/Normalization_(statistics) Normalization is subtracting the mean then dividing by the standard deviation. What is the name of just subtracting the mean?
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Neural networks: normalizing data which already lies in [0,1]

I am running neural networks with a sigmoid activation function, and have output data which already lies within [0,1]. However, the minimum value of the data is around 0.05, and the maximum is about 0....
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In a multioutput deep learning model, is there a benefit to normalizing the output dimensions if they are of different magnitudes?

I am building a multi output deep learning model where the output consists of five dimensions (the specific architecture is a modification of YOLO). These have different magnitudes (ranges: [0, 1.2], [...
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What does it mean for a binomial distribution to be normalized over something?

This is my first post on cross validated (and my first attempt at statistics!) so please let me know if I haven't been clear enough with my question. So I was learning about Bayesian Parameter ...
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normalize data with KPI value

I have this sample of a dataset that show for each number of ads showed to the user, the number of click and the KPI value (CTR): ...
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How to transform a set of negative values to obtain a normal distribution? [duplicate]

I am having quite a few problems with transforming a set of data with values between -1 and 0, as I need to normalise them. I tried to use the following formulae: [(𝑥−min(𝑥))/(max(𝑥)−min(𝑥))] AND ...
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When should you normalize data?

I have 2 sets of prediction data and both sets have different scale, for example, one of it is in the range of 1e-3 value while the other is between 0-2, both have negative values. I would like to ...
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Is L2 normalization of rows followed by min/max scaling the same as mean-centering and unit variance?

I'm following this guide on detecting anomalies using autoencoders. The section titled "Normalising & Standardising" seems to be describing normalization in terms of scaling and shifting ...
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Standardisation with respect to controls

When performing analyses using polygenic risk scores (PRS), Why is it important to standardise/normalise PRSes using mean and sd derived from control samples, before performing analyses, such as ...
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Normalization of time series data for input into Neural Network, values will vary over time

I was reading up on how to normalize stock time series data for input into a neural network. What I've seen suggests things like min-max normalization and z-score normalization. The issue I see is, ...
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Normalization of coordinate in panel data? (timeseries)

I'm working with the neural networks and motion prediction on mesh data. Objective of neural network is to predict $T_p$ frames in the future, given previuos $T_s$ frames. We have let's say 10 objects....
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How to do data imputation and normalization when using polynomial regression?

The question is about the practical use of polynomial regression. Let's say there is a dataset with columns A, B, T where T is a dependent variable, A and B are independent variables. A and B contain ...
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Is it possible to normalise data between -1 and 1? [duplicate]

I'm surprised that google doesn't yield any results whatsoever for this question. Say I have values between 20 and 140, is it possible to normalise that data to be between -1 and 1?
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Variance properties of transformed p-values using the standard normal distribution

I am searching for a reference or proof for the following situation: Let t_df denote a t-test statistic for given degree of freedom df. The null hypothesis being tested is H_0: $\delta$ <= 0. ...
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How to adjust for the baseline difference between groups for a histogram

I have data from an experiment that tested responses to stimuli in females and males. I measured baseline for both groups prior to presenting the stimuli. The baseline for males is higher than for ...
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General rule for new decision boundary after normalizing data?

Many classifiers, such as Support Vector Machines (SVMs) generalize better when the input features are normalized. Is there, however, an intuitive visualization/explanation of the map of the original ...
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How do you scale the activation function of an auto-encoder when using a custom normalization fitted on the data?

I'm working on a convolutional auto encoder. The input is an image The output is a reconstructed image During the training phase, we feed the same image in and out The loss is the Mean Squared Error ...
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When to Normalize Training Data

As I have seen so far, it seems like choices such as whether or not to normalize your training data are made based on the results you get after evaluating your model on the test data. Is there a more ...
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Weighted adjacency matrix normalization for GCN, how to normalize? what about self-loop values?

I am implementing a GCN that will work on a weighted graph. The edges' weights are in the range [1, 250]. When it comes to normalizing the adjacency matrix for GCNs, the standard formula of a ...
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How to normalize mortality in determining lethal concentration of toxins or pesticides?

A certain set of pesticide concentrations is selected to dose the animals to determine its acute toxicity. Technically, with increasing concentrations, the mortality should increase, but that does not ...
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Normalised score from multiple other scores

I am trying to understand the best way to create a (normalised?) score with which I can used to compare various items. Each item has various data attributed to it, typically rates so percentage scores ...
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Use cases of L1, L2 and Max normalisation of a vector

I have a vector; and I can normalize it by dividing with either: Max of the values of the vector L1 norm of the vector L2 norm of the vector The math behind each of the above approaches is nicely ...
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Geometric mean of log10 values

I have the following series of log10 numbers: ...
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Contradictory behaviour on sums of poisson variables

Trying to solve this problem of mine (you don't actually need to read the linked problem, the problem is rephrased below with a different assumption): Estimating a sample size such that its sum has ...
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Can a heterogenous approach to data normalization yield a better consensus learning result?

The following makes sense intuitively, but I cannot find a reference: Let's say I've got 3 datasets, for example differential gene expression, of related experiments, where normalization was done in ...
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Can anybody think of a transformation to normalize these data?

I want to do a multiple linear regression but I do not find any way to normalize the data I have. This is the distribution without transformation. The data contains a good number of zeros. When I ...
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Do we do data normalization just in case our initial data has gaussian distribution?

We always do Data Normalization of our data when we have different ranges, and I found that normalization is just a translation followed by a multiplicative scaling, so it does not really change your ...
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Nodes' attribute scaling/normalization before graph embedding learning - GNN?

In a node classification setting, is it require to normalize/scale graph node attributes before learning node embeddings using graph neural networks? Why?
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Why not both standardize and normalize features for machine learning?

If one has data that's assumed to be normal distributed and want to use it as input in a machine learning model, why not first standardize the data and then normalize (min max scale it between zero ...
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Scaling autocorrelated features

If I have a bunch of autocorrelated features (for example, temperature, rainfall) that I want to use to predict a dependent variable, how should I scale these autocorrelated features before passing ...
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Normalizing daily data to simulate, and then de-normalize, N-day data

I am attempting to model an N-day joint density for a portfolio of assets. To keep things simple, I have assumed a Gaussian copula but have gotten pretty unrealistic results assuming lognormal returns....
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Normalising disjoint scores into single percentage

First time posting so forgive my appalling knowledge of the correct terms. Image we would like to score a house on its security based on three factors: Number of doors with locks. This is a fraction ...
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When would you need scaled error between different time series evaluations?

Let's say we have 3 time series for three different fruits sales over one year. Although they are all fruits, their daily sold volume is very different. For example, imagine ...
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Normalize binary variable in MCDM?

I have some data with a couple of binary but also continous variables. For MCDM, there are different normalization techniques such as Max, Max-Min or Vector Normalization. However, I'm wondering if it ...
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When doing regression, is it possible to normalize just certain variables?

I have a dataset containing categorical variables (0-1), variables with range 0-99 and an income variable with range 0-100k. I want apply linear regression, logit etc but I guess that different scales ...
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Incorporating gyroscope data information in accelerator data in iPhone

I am performing Human Action Recognition based on IPhone sensor data. One approach that worked excellently was plotting this data and using image-based models. Currently, I am training my models on ...
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Statistical Significnace of a Survey Analysis

We understand that the bigger the sample size, the more reliable is the result of the data analysis of a survey. How do you determine with confidence that a survey results are valid and representative ...
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how to increase location accuracy?

I am developing a website for student attendance if a student is present in classroom in given time and he/she fill attendance by pressing a button then his/her attendance should be marked as present. ...
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Mahalanobis vs centering / standard deviations

Is there a difference whether to use a Mahalanobis distance or transform the data via centering (and normalization) when you are interested in calculating distances? This means, if you are interested ...
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Understanding centralization and normalization

I have the following task where I am given an audio signal: Center the signal (subtract the mean value) and normalize to a dynamic range of -1 to 1 (divide be maximum of the absolute value). If I ...
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I have difficulties to understand the input normalization - density estimation in the same context in ML or DL

I know (by experimenting with different ML and DL algorithms) that input normalization helps to improve the performance of the model. When we do normalization in training, with the same mean and ...
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Should I center and standarize in a PCA of relative growth?

Im quite familiar with PCA, but as a non-pure-statician I have come here to ask for help and comments. I have a dataset with multiple meassures (End and initial for a lot of meassures), and the time ...
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