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 ...
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 ...
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, ...
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 ...
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 ...
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 ...
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 ...
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-...
2k views

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 ...
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 ...
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 ...
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 ...
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 ...