Questions tagged [normalization]

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

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336
votes
8answers
1.3m views

How to normalize data to 0-1 range?

I am lost in normalizing, could anyone guide me please. I have a minimum and maximum values, say -23.89 and 7.54990767, respectively. If I get a value of 5.6878 how can I scale this value on a scale ...
155
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5answers
251k views

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, ...
51
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7answers
102k views

Is it a good practice to always scale/normalize data for machine learning? [duplicate]

My understanding is that when some features have different ranges in their values (for example, imagine one feature being the age of a person and another one being their salary in USD) will affect ...
7
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1answer
3k views

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 ...
75
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6answers
103k views

What are good initial weights in a neural network?

I have just heard, that it's a good idea to choose initial weights of a neural network from the range $(\frac{-1}{\sqrt d} , \frac{1}{\sqrt d})$, where $d$ is the number of inputs to a given neuron. ...
72
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9answers
109k views

How and why do normalization and feature scaling work?

I see that lots of machine learning algorithms work better with mean cancellation and covariance equalization. For example, Neural Networks tend to converge faster, and K-Means generally gives better ...
93
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2answers
212k views

Why do we need to normalize data before principal component analysis (PCA)? [duplicate]

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 normalization? ...
53
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2answers
68k views

Are mean normalization and feature scaling needed for k-means clustering?

What are the best (recommended) pre-processing steps before performing k-means?
55
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8answers
108k views

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. ...
42
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3answers
18k views

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 ...
56
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1answer
28k views

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 ...
61
votes
4answers
36k views

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 ...
25
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3answers
73k views

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 ...
63
votes
2answers
110k views

How to normalize data between -1 and 1?

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 positive values in my data matrix.
25
votes
1answer
12k views

Converting (normalizing) very small likelihood values to probability

I am writing an algorithm where, given a model, I compute likelihoods for a list of datasets and then need to normalize (to probability) each one of the likelihood. So something like [0.00043, 0.00004,...
3
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1answer
999 views

Neural Networks input data normalization and centering

I'm learning Neural Networks and I grasped the algebra behind them. I'm now interested in understanding how normalization and centering of the input data affect them. In my personal learning project (...
34
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3answers
43k views

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 ...
21
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4answers
22k views

“Normalizing” variables for SVD / PCA

Suppose we have $N$ measurable variables, $(a_1, a_2, \ldots, a_N)$, we do a number $M > N$ of measurements, and then wish to perform singular value decomposition on the results to find the axes of ...
4
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1answer
1k views

scaling for SVM destroys my results [duplicate]

I'm applying standard 0-1 scaling of features before SVM classification for financial data but the results are worse. This is the results before scaling ...
6
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2answers
879 views

Is the normal distribution a better approximation to the binomial distribution with proportions near or far from 0.5?

From the Online Stat Book: I don't understand this: The accuracy of the approximation depends on the values of N and π. A rule of thumb is that the approximation is good if both Nπ and N(1-π) ...
32
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9answers
41k views

How to represent an unbounded variable as number between 0 and 1

I want to represent a variable as a number between 0 and 1. The variable is a non-negative integer with no inherent bound. I map 0 to 0 but what can I map to 1 or numbers between 0 and 1? I could use ...
10
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1answer
12k views

Standardizing features when using LDA as a pre-processing step

If a multi-class Linear Discriminant Analysis (or I also read Multiple Discriminant Analysis sometimes) is used for dimensionality reduction (or transformation after dimensionality reduction via PCA), ...
2
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2answers
1k views

In Machine learning, how does normalization help in convergence of gradient descent?

I have read in an article that normalization helps gradient descent to converge faster in machine learning. But I cannot understand why is that. Any idea?
3
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1answer
325 views

Effect of rescaling of inputs on loss for a simple neural network

I've been trying out a simple neural network on the fashion_mnist dataset using keras. Regarding normalization, I've watched this video explaining why it's necessary to normalize input features, but ...
35
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5answers
31k views

Should you ever standardise binary variables?

I have a data set with a set of features. Some of them are binary $(1=$ active or fired, $0=$ inactive or dormant), and the rest are real valued, e.g. $4564.342$. I want to feed this data to a ...
18
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4answers
9k views

Normalization prior to cross-validation

Does normalizing data (to have zero mean and unity standard deviation) prior to performing a repeated k-fold cross-validation have any negative conquences such as overfitting? Note: this is for a ...
2
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1answer
7k views

De normalize predicted value

Alright so i have found this really good answer on how to normalize my data. I implemented @user25658 's code into my own project successfully, trained a linear model and used it to make a ...
2
votes
1answer
6k views

With the Naive Bayes classifier, why do we have to normalize the probabilities after calculating the probabilities of each hypothesis?

In the Naive Bayes classifier, why do we have to normalize the probabilities after calculating the probabilities of each hypothesis?
6
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3answers
2k views

Raw data outperforms Z-score transformed data in SVM classification

I've been trying to perform a binary classification using an SVM classifier (scikit-learn's SVC with RBF kernel). I have a sample size of about 100, with about 70 features each. The features are of ...
29
votes
5answers
46k views

When should I apply feature scaling for my data [duplicate]

I had a discussion with a colleague and we started to wonder, when should one apply feature normalization / scaling to the data? Let's say we have a set of features with some of the features having a ...
33
votes
2answers
36k views

Is cosine similarity identical to l2-normalized euclidean distance?

Identical meaning, that it will produce identical results for a similarity ranking between a vector u and a set of vectors V. I have a vector space model which has distance measure (euclidean ...
24
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2answers
58k views

When to normalize data in regression? [duplicate]

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 ambiguous answer "depends on the data". ...
29
votes
2answers
38k views

Is it essential to do normalization for SVM and Random Forest?

My features' every dimension has different range of value. I want to know if it is essential to normalize this dataset.
13
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6answers
14k views

A robust (non-parametric) measure like Coefficient of Variation — IQR/median, or alternative?

For a given set of data, spread is often calculated either as the standard deviation or as the IQR (inter-quartile range). Whereas a standard deviation is ...
16
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1answer
13k views

How does quantile normalization work?

In gene expression studies using microarrays, intensity data has to be normalized so that intensities can be compared between individuals, between genes. Conceptually, and algorithmically, how does "...
12
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2answers
17k views

Standardization vs. Normalization for Lasso/Ridge Regression

I am aware it is common practice to standardize the features for ridge and lasso regression, however, would it ever be more practical to normalize the features on a (0,1) scale as an alternative to z-...
8
votes
3answers
7k views

In general, does normalization mean to normalize the samples or features?

I'm just getting into machine learning, and I have seen two conflicting practices for normalization. To be concrete, let's suppose that we have a $n \times d$ matrix containing our training data, ...
8
votes
1answer
26k views

Logistic regression and scaling of features

I was under the belief that scaling of features should not affect the result of logistic regression. However, in the example below, when I scale the second feature by uncommenting the commented line, ...
11
votes
1answer
8k views

Dynamic Time Warping and normalization

I'm using Dynamic Time Warping to match a "query" and a "template" curve and having reasonable success thus far, but I have some basic questions: I'm assessing a "match" by assessing whether the DTW ...
2
votes
1answer
8k views

Min-Max scaling on Z-score standardizd data?

For a specific task of score fusion I need to test my data on some different normalization techniques like typical Z-normalization or Sigmoid-normalization. This is my first step to do. For a second ...
8
votes
2answers
8k views

Should I ever standardise/normalise the target data/ dependent variables in regression models?

After standardising the explanatory variables the difference in magnitude between the explanatory variables and the target data is ~3 orders of magnitudes. I want to know if transformation of the ...
5
votes
1answer
5k views

Scaling/Normalization not need for tree based models

I could not find a good answer/reference that can explain why rf/decision trees/gbm are not susceptible to the scale of values of numerical variables. My sense is that since boosting methods ...
25
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1answer
1k views

Random matrices with constraints on row and column length

I need to generate random non-square matrices with $R$ rows and $C$ columns, elements randomly distributed with zero mean, and constrained such that the length ($L_2$ norm) of each row is $1$ and the ...
7
votes
4answers
22k views

What are the primary differences between z-scores and t-scores, and are they both considered standard scores?

We are currently converting student test scores in this manner : ( ScaledScore - ScaledScore Mean ) / StdDeviation ) * 15 + 100 I was referring to this ...
6
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2answers
36k views

What does it mean to use a normalizing factor to “sum to unity”?

Would you also be able to provide an example? I have very little mathematical/statistical knowledge and have never understood normalization.
5
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2answers
9k views

How to standardize data for hierarchical clustering?

When running hierarchical clustering analysis of a matrix of individuals x samples (e.g., employee performances across different days), there are several ...
4
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2answers
17k views

How to transform negative data to be homoscedastic

I have a bunch of data that's both positive and negative. Its calculated from the residuals of an ANOVA (i.e. specific leaf area calculated as the residuals of an ANOVA of leaf area with leaf blade ...
8
votes
1answer
368 views

Follow-up question: When should you center your data & when should you standardize?

I have a follow up question to MånsT's reply to the "When should you center your data & when should you standardize"-question. ( I cannot leave a comment as I am below the magic "50 ...
3
votes
1answer
705 views

What are appropriate methods for preparing categorical features for recurrent networks to ensure efficient backpropagation?

Given a 1D sequential categorical input variable, e.g. [rainy, sunny, rainy, cloudy, cloudy], with a small domain {rain, sunny, cloudy}, what encoding methods (e.g. one-hot, dummy, binary) and what ...
2
votes
2answers
2k views

Confidence interval of ratio estimator

As an example, consider a program that executes on two computers, A and B. Measuring the execution time of 3 executions each shows the following results: System A: 10s, 10s, 4s System B: 8s, 8s, 2s ...