Questions tagged [feature-scaling]
The feature-scaling tag has no usage guidance.
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Feature scaling for recurrent auto encoder network
I have 5 dimensional input to my model. The model in question is recurrent autoencoder there were are talking about unsupervised learning.
Those features are physical variables, and for my purposes it ...
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How should uncertainties be treated when scaling data for optimisation
I have a large dataset for which I am using Bayesian statistics for parameter estimation and model selection (using MultiNest for more detail).
This involves setting a prior over which the nested ...
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Do we need to scale the encoded data also? [duplicate]
I have a dataset with both categorical and numerical values. First I encoded the categorical data using one-hot encoding. Then the next step is to feature scaling. For that, I am going to use ...
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Data scaling for hidden Markov models?
I understand that scaling data is important for certain machine learning algorithms, and the idea makes sense. I've found this great description of the processes here https://ourcodingclub.github.io/...
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Obtain one single growth curve from various replicates in a growth experiment
I am performing a yeast growth experiment measuring areas of growth across a time interval, therefore having area (%) as my dependent variable and time (min) as my independent variable. I have three ...
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What to do after fitting regression model on scaled data? [duplicate]
I have created a multiple-linear regression model on some data. The different explanatory variables were all in different units, so it seemed appropriate to scale the data. I've fitted a model and the ...
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Understanding the Normalization/Standardization of geospatial coordinates
I'm building a neural network to predict future [latitude,longitude,altitude], and am having trouble dealing with the features. I've reviewed the answers to the ...
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Dealing with 0's in loglog regression by using indicator functions I(x > 0)?
Assume we want to estimate the following model
$y = e^{\beta_0} * x_1^{\beta_1} * x_2{\beta_3}$ which we can linearize into
$\log(y) = \beta_0 + \beta_1 * \log x_1 + \beta_2 * \log x_2$
Assume that ...
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loglog regression with 0's in IV's
Assume we have 2 predictors $X_1$ and $X_2$ and an outcome $Y$ that we wish to model with the following function
$y = e^{\beta_0} * X_1 ^{\beta_1} * X_2^{\beta_2}$
Also assume that we have some priors ...
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Interpretation of parameters in loglog-regression with scaled variables
Consider e.g the model $y = e^{\text{trend} + \text{seasonality}} \cdot \prod_{k \in \text{channels}} x_k^{b_k}$
where $i$ constrained $0 < b_k < 1$ (to capture diminishing marginal returns)
...
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Do we need to scale our features before applying ICA, like in PCA?
I am reasonably certain that we don't need to scale data before applying ICA, like we do for the PCA. In PCA we do this because it assumes normal distribution of the features, and in ICA we don't ...
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Why feature scaling does not affect prediction output in regression?
I was modelling a linear regression (OLS) and tried using scaling techniques on the predictor variables. I could see the range of the variables change, however the prediction results remain the same. ...
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Do you lose information when you encode numerical columns with two values?
Sometimes I have numerical columns that are composed of two unique values. For example, a value from the set $\{0.1, 5.4\}$ in every cell, or $\{-1, 0\}$ in every cell. I typically scale these columns ...
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Scaling by percentage - is this appropriate given this situation?
Let's say I have a range of formulations, and each formulation contains a different starting rate of water "x", and I want to test how fast the formula dries out over time (ie. loss of water ...
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Do we lose information when we normalize an image? [closed]
Before training a machine learning algorithms, it is advisable to perform feature scaling. Suppose we have a "toy" dataset where each image is composed of two pixels $x_0$ and $x_1$. Lets ...
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