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How to represent time series with exponential growth as normalised input?

Many ML models perform better when the input is normalised like stay between 0..1 interval. How the stock time series with exponential like growth could be represented? It could be normalised into ...
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When shall I normalize/standarize the variables of a dataset? before or after subsetting a dataset?

I am using a subset of a large dataset to run panel regressions in R. Because variables range differently, I have to re-escale them (normalize/standarize). The problem is that I do not know whether I ...
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23 views

Feature Scaling in Regression

I have a dataset in which each sample has only two features. I designed my own gradient descent algorithm, and applied it to my dataset. However, I could not obtain a result. Then, I printed the ...
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Use standard scaler in combination with min max?

I asked a colleague if it would be wrong to use feature scaling in decision trees since it is not required by the algorithm. He said that not only I could, but also it is a good practice. However, he ...
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29 views

What units is my mean squared error if I center and scale my training data?

I have a KNN model that I used to predict the close price on houses. ...
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What scaling to choose for data that is always >0? Does it matter?

If I have something like stock price or income that cannot be negative, what scaling should I pursue? Is there research that suggests that centering it at 0 will cause issues? I'd assume so but I ...
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Feature scaling/normalization follwing a sample level normalization

In micro-array or many similar platforms per sample normalization (z-score) is a common practice to minimize the impact of outlier values. Do we need a feature scaling after this ? In Details : I ...
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63 views

Scaling data with different importance

I have 9 attributes: x1,x2,x3,x4,...,x9 and I know that the attributes x9 must have the same value in a cluster and the attribute X1 have more importance than others (x2,...,x8) I'm using Euclidean ...
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40 views

When to scale data, if I have features of all numeric values? [closed]

i'am working on a case study, i'am having train data in which there are 45 columns out of which 28 are useful, case study is related to loan approval. all the columns in dataset are int64 format. ...
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Z score standardisation vs min-max scaling for feature selection

I am applying l1 norm on the input weights of a single layer MLP. I wanted to know if I should standardize or min-max scale ([0 1] feature scaling) my input data?
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Which machine learning algorithms get affected by feature scaling?

Which of the following machine learning algorithms will be affected if we apply feature scaling? Naïve-Bayes k-Nearest Neighbor (KNN) Support Vector Machine (SVM) Decision Trees Neural Network (...