1 vote
544 views

### 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 ...
28 views

### 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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5k views

### 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 ...
3k 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 ...
• 1,159
382 views

### "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 ...
1 vote
3k views

### 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 ...
• 13
185 views

### 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 ...
• 165
3k views

### 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 ...
5k views

### 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 ...
• 1,086
1k views

### 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? ...
• 561
107 views

### When not to use standardization on variables [closed]

Can someone give me a counter example of when we should not use standardization on variables? I understand what standardization is but i am not getting the point why we need to standardize variables? ...
• 225
11k views

### If you standardize X, must you always standardize y?

Related reading: When conducting multiple regression, when should you center your predictor variables & when should you standardize them? When and how to use standardized explanatory variables in ...
• 792
3k views

### 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 ...
42k views

### 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 ...
• 718