Linked Questions
18 questions linked to/from What algorithms need feature scaling, beside from SVM?
11
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2
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Whitening/Decorrelation - why does it work?
Given some whitening transform, we change some vectors $\textbf{x}$, where features are correlated, into some vector $\textbf{y}$, where components are uncorrelated. Then we run some learning ...
- 11
1
vote
1
answer
554
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Should you scale the dataset (normalization or standardization) for a simple multiple logistic regression model?
I have read a lot of conflicting literature about scaling the dataset (using methods such as normalization or standardization) for a multiple logistic regression model, and I am wondering if scaling ...
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41
votes
2
answers
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Dropping one of the columns when using one-hot encoding
My understanding is that in machine learning it can be a problem if your dataset has highly correlated features, as they effectively encode the same information.
Recently someone pointed out that ...
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0
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0
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Why are linear/logistic regression and naive bayes called "parametric" while SVM, random forests, neural nets are not? [duplicate]
This table is mentioned in What algorithms need feature scaling, beside from SVM?
It says that linear regression, logistic regression, and naive bayes are parametric, while KNN, decision trees, ...
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4
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1
answer
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Does XGBoost require standarized data?
In related question (What algorithms need feature scaling, beside from SVM?) every answer stated that XGBoost doesn't require any standarization, but someone wrote in comment that:
+1. Just note that ...
- 42k
6
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2
answers
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Data matrix, predictor matrix, observation matrix, model matrix, and design matrix. What do they mean?
Is there a clear distinction between these terms? To the best of my knowledge:
Suppose we have $N$ observations and $p$ predictors.
predictor matrix $\in \mathbb{R}^{N\times p}$ is synonymous to ...
- 73.6k
2
votes
2
answers
3k
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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 ...
- 11.5k
5
votes
1
answer
383
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"Joint" dummy variables for two different variables
I am supposed to show the hazard ratio (HR) stratified by gender (1= female vs. 2= male) and age groups (quartiles, 1-4)*. The combination "female" and "first quartile of age" is supposed to be the ...
- 73.6k
80
votes
8
answers
119k
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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 ...
- 5,141
1
vote
1
answer
3k
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Multiple regression of variables with different units
I'm new in statistical modelling and using R, so please excuse my mistake for this question.
I want to make multiple regression model with these variables:
Revenue (in million USD) as dependent ...
- 116k
2
votes
0
answers
117
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Relation between Range of feature and Range of parameters in Logistic Regression?
It may be a very basic question but as a beginner in MachineLearning I still cannot figure out the answer.
In Andrew Ng's machine learning course he explained why we need feature scaling in http://...
- 73.6k
0
votes
1
answer
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Should feature scaling be used while using unsupervised algorithms?
I have read many articles and resources about using feature scaling and when to use it, in particular two answers on this website as well-
When should I apply feature scaling for my data?
What ...
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4
votes
3
answers
3k
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Why does scaling the features affect the prediction of a regression?
I'm working on a regression problem using the support vector regression model from sklearn and using MinMax to scale the features, but by using it I get a different result for the regression, does ...
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-1
votes
1
answer
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Why do we normalize variables in classification, but not regression
I understand that we need to normalize data for classification problems because otherwise the variable with the larger scale will dominate the result. But why don't we normalize for linear regression?
...
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69
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3
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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?
