Feature scaling is a data pre-processing step where the range of variable values is standardized. Standardization of datasets is a common requirement for many machine learning algorithms. Popular feature scaling types include scaling the data to have zero mean and unit variance, and scaling the data between a given minimum and maximum value.
Questions tagged [feature-scaling]
263 questions
11
votes
6 answers
When should I NOT scale features
Feature scaling can be crucially necessary when using distance-, variance- or gradient-based methods (KNN, PCA, neural networks...), because depending on the case, it can improve the quality of results or the computational effort.
In some cases…
Romain Reboulleau
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1 answer
Normalized output of machine learning
For machine learning, we need to normalized the inputs (features) for good results.
Do we need to normalize the output as well during training?
william007
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Scaling dependent variables
In a regression model is there a need to scale/normalize a dependent/response variable when doing the same for independent variables? If not then why?
An example will be helpful.
Sai
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can i scale features within each input catgory differently
i am trying to predict laptop prices from previous sales. based on a text description of the laptop condition and the sale price. the affect of the condition on the final price is going to be different for a high value laptop and low value laptop.…
user2897377
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Why is the testing set standardized using fit values from the training set
I was wondering, in the feature scaling part of data preprocessing, why is the data in the testing set standardized using the fit values from the training set? Why aren't the fit values recalculated separately for the testing set and then used for…
dkapur17
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Should I scale my features?
I have a dataset that looks something like this;
ID | Location | Job_title | blue_jumper | red_jumper | yellow_jumper | green_jumper |…
Taylrl
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Finding out the scale when using MinMaxScaler()
I am using the MinMaxScaler() of sklearn to scale my features before using kmeans.I needed to find the scale used.
from sklearn import preprocessing
scaler = preprocessing.MinMaxScaler()
scaler= scaler.fit_transform(finance_features)
print…
PiGamma
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