Linked Questions

366
votes
7answers
325k views

When conducting multiple regression, when should you center your predictor variables & when should you standardize them?

In some literature, I have read that a regression with multiple explanatory variables, if in different units, needed to be standardized. (Standardizing consists in subtracting the mean and dividing ...
0
votes
1answer
368 views

Why does feature scaling improves accuracy? [duplicate]

With feature scaling we just change representation of the data. This can make our model run faster but how this can improve accuracy? It is the same data after all. When I train my SVM without ...
1
vote
0answers
453 views

Why centering the data for machine-learning? [duplicate]

earners, I thought Friday was a good excuse to do a very basic question, which still makes me wonder. Why do we need the centering part in standard normalization? Assuming normalization means here, ...
0
votes
0answers
144 views

Usefulness of Z-normalization in Machine Learning [duplicate]

Z-normalization means rescaling the feature $X$ by subtracting the average $\mu$ and dividing by its standard deviation $\sigma$, i.e., $(X-\mu)/\sigma$. What is the usefulness of normalizing data ...
0
votes
0answers
43 views

Can z-score transformation ever be undesirable? [duplicate]

I am new to the field and learned about scaling (by z score transformation) of data. While it seems a super useful and universal technique, I have learned that no technique is applicable to each and ...
0
votes
0answers
7 views

Should I normalize the data? [duplicate]

I have four int columns with two of them having a value in 10s, and the other two have it in 100s. Should I, for the ease of applying the following algorithms, normalise the data, or would it not have ...
57
votes
5answers
98k views

Is it important to scale data before clustering?

I found this tutorial, which suggests that you should run the scale function on features before clustering (I believe that it converts data to z-scores). I'm wondering whether that is necessary. I'm ...
0
votes
2answers
3k views

(Deep) Neural Networks/MLPs: Should I normalize/scale my input features when the units of the features are meaningful?

Until now, I always normalized or standardized my features individually before feeding them into a neural network. But at my current project I have features, which in huge parts have the same unit (US-...
7
votes
0answers
2k views

Normalization vs Standadization [closed]

Of the two best known techniques for feature scaling in Machine Learning: Normalizing a feature to a $[0, 1]$ range, through $x - x_{min} \over x_{max} - x_{min}$ or Standardizing the feature (also ...
4
votes
0answers
763 views

Effectiveness of Standardization and Normalization in Machine Learning

I am just studying the basics of machine learning and had a question about the standardisation and normalisation of the features and its effectiveness. I have read this CrossValidated question and ...
1
vote
1answer
182 views

Why is there a performance difference before and after scaling and normalisation?

Here is my understanding of scaling, "The main advantage of scaling is to avoid attributes in greater numeric ranges dominating those in smaller numeric ranges." Let's take k-nearest neighbours: If ...
0
votes
1answer
169 views

Scaling data with a time feature

I'm going through a solution of the bike sharing demand problem and one moment about scaling data is unclear to me. Concretely, why do we fit scaler only on our training data instead of the whole ...
0
votes
2answers
130 views

Problem using new input data on machine learning classifier

We have built a machine learning classifier for some experimental data. During this process, we performed discretization on the continuous target variable using its median as a threshold. We would ...
0
votes
1answer
65 views

Why is scaling usually done within the range of -1 and +1 in machine learning?

I observe that in machine learning, scaling is done to make the numeric input fit within the range from -1 to +1. Why not a bigger range like -10 to -10? If smaller range is better, then why not -0.1 ...
0
votes
1answer
56 views

scaling/centering - always necessary?

For regression analysis (mine specifically multinomial logit) with the objective of prediction, is it truly necessary to scale variables before fitting the model? What if I want to apply ...