Questions tagged [feature-scaling]

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How can I normalize extracted latent space from Autoencoder?

I'm currently training two LSTM-autoencoder for each simulation data and real data. The data are vectors and I wanna match the latent space values of each encoder. Although it converges well, but I ...
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scaling features for LASSO variable selection

I am interested in performing LASSO regression for the purpose of variable selection. The response variable is categorical (3 classes) and most of the predictor variables are categorical. Most ...
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WHEN (not should) to scale features [closed]

In machine learning, how do you know when you will need to scale data? I am not asking if I should scale data! I am asking at what range differences should I scale data? I have not found anything that ...
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when to scale features in machine learning [duplicate]

In machine learning, how do you know when you will need to scale data? I am not asking if I should scale data! I am asking at what range differences should I scale data? I have not found anything that ...
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Feature scaling with categorical variables

I've a dataset with numeric features and categorical features. For the latter I created dummy variables. I've to implement a KNN model so I've to scale my variables. My doubt is: how to handle these ...
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Best formula to normalize non linear scores to scale of 1-100

I have lists of scores, which can be very non linear. For example: 20332 18000 599 39 19 2 I need to normalize these scores to a 1-100 scale so I can do some ...
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what if range of normalized of data in machine learning goes beyond?

Normalization in machine learning is the process of translating data into the range 0 -> 1 or -1 -> 1. What if the values goes above 1 or below -1. What does that mean? Is it again an outlier? ...
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Computing distance and standarization of features

Intro: suppose we have $n$ observations with $m$ features, represented by a $n\times m$-matrix $X$, and two specific points $x,y\in\mathbb{R}^m$, and we are interested in distances between $X$ and $x,...
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normalize data with KPI value

I have this sample of a dataset that show for each number of ads showed to the user, the number of click and the KPI value (CTR): ...
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Is feature scaling needed for dataset containing non-categorical and categorical independent variables?

I have a dataset containing 800+ label encoded (2-level as 0 or 1) categorical features and 4 non-categorical numerical features. The dependent variable is a non-categorical numerical value. Should I ...
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2 votes
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Min-max scaling vs standardizing in LASSO

I know that it is recommended to have features on the same scale for LASSO, such that the scale does not affect the penalty. However, does it matter whether or not features are scaled using $\frac{x-\...
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Feature scaling of categorical and ordinal variables in Cox regression

I have a dataset with nominal (unorderable categories), ordinal (orderable categories), and continuous/numerical variables. I am performing Cox Proportional Hazard Regression using the scikit-survival ...
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Non-linear scaling of data

I have a set of numbers in a range between 1 to 5, heavily skewed towards 3. How can I (preferably in python) non-linearly scale the numbers so the difference between the numbers is more pronounced? I ...
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Correct Way to analize the time series data where we cant calculate the central tendency

If we are given a dataset of real-time sensors, we need to normalize the features. We might not be calculating the central tendency of the whole data coz it's been updating every 5 secs. So if we ...
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1 answer
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How to scale data for model retraining on production?

Let's say I have a basic regression model being used in production and now I want to implement periodical model retraining (i.e. once a month) where I take a batch of new data from last month and fit ...
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Scaling autocorrelated features

If I have a bunch of autocorrelated features (for example, temperature, rainfall) that I want to use to predict a dependent variable, how should I scale these autocorrelated features before passing ...
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Scaling a power law distribution for k-means clustering

For my project I want to group some products by using a few variables. For grouping, I am using k-means clustering. One of my variables is a metric called CR (conversion rate) which takes values ...
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normalizing and scaling are different?

This is the original data histogram, I have a data set and plot by DataFrame.hist(): After that I applied the zscore function to my data set and plot this histogram: After I have applied zscore, I ...
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Standardisation and Logistic Regression

I read that "Standardization isn’t required for logistic regression. The main goal of standardizing features is to help convergence of the technique used for optimization." Although Logistic ...
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Mix of numerical and categorical features - to scale or not to scale?

I have the means of my features like this of an employee dataset: ...
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Is there any paper explaining which machiine learning algorithms needs scaling in data pre-processing?

I can see on many blogs and tutorials that data scaling (e.g standardization) allow better performance for logistic regression and neural networks, but does not improve tree-based models (...
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Decision trees and Feature Scaling for regularization

For all tree-based models like xgboost, lightgbm, random forest, they do not require feature scaling given the nature in which they compute their splits. However, when you perform regularization, one ...
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Should I scale my data before a Cartesian-to-spherical conversion?

If I have three features, should I scale them before converting them to a spherical coordinate system? I have been working on a ternary classification problem. My data is high-dimensional, so I've ...
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Standardizing and Normalizing independent variables

I understand that, given a single dependent variable (ex: price) and several different independent variables (ex: area, number of rooms, Number of floors, etc.) with different ranges and units, ...
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How to immune the model to possible changes in feature value ranges?

I'm working ML model to classify a binary class variable for an input signal with variable length. I extract several length-independent features from the signals to construct the dataset for training, ...
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Should I scale my data before time series PCA?

I am doing PCA on 22 time series. All the series are log transformed and in their stationary form. The plot of all the input series is given below. My main aim is to forecast GDP. I noticed that ...
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Principal Component Analysis to Time Series Data (PCA)

What is the procedure to do PCA on time series data? I followed the following method and I want to know whether it is correct Scaled the stationary time series Did PCA on the series obtained by ...
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Scaling columns pandas DataFrame

For a dataset having different numeric columns, they usually have different range and distributions. As an example, I have used the Iris dataset. The distributions of it's 4 columns are shown: My ...
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Look ahead bias by standardization of a time series in predictive models?

I'm using some machine learning models to predict future values of a time series (stock returns). In the data preprocessing step I'm standardizing all variables (incl. the target variable) using ...
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Different prediction performance when scaling data ALREADY between 0 and 1

I am fitting a LSTM neural network for time series forecasting on realized volatility data available here. I am only using one of the variables in the dataset, namely 'rv5' or the 5-minute realized ...
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z-score VS min-max normalization

Working with data that use different dimensions, you do not want that one dimension dominate. This means feature scaling! A very intuitive way is to use min-max scaling so you scale everything between ...
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4 votes
2 answers
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Scaling features for neural network input

I have a df with many features that take both negative and positive values.For example a feature may have values in range (-10 , 10).For each feature which has negative values the negative sign means ...
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Scaling different datasets for comparison - Increasing signal 'gain' / upscaling

I have 4 protein microarray datasets that I am trying to compare. I have concocted a method of aligning them all and comparing them all on a continuous scale which seems to have worked well. These ...
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Should neural net features be scaled before applying t-SNE?

It is considered good practice to scale the data (zero mean and unit variance) before using an algorithm like t-SNE or UMAP, such that all features are given the same importance If the features are ...
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Should I scale time-series features for supervised learning classification?

I couldn't find an answer to this in the archives so posting this here. I am currently building out a supervised-learning / classification pipeline for time series forecasting (e.g. predicting the ...
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percentage features VS discrete attribute?

I have a dilemma about some of the features in a dataset. The dataset contains some discrete features. I can represent these features as integers or I can divide them by the total sum and multiply by ...
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Is it correct to calculate the covariance matrix on scaled features?

I have a set of features with the following covariance matrix: ...
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Feature importance after polynomial regression - when should I apply scaling?

I have a dataset with $n$ numerical features $x_1,\dots,x_n$ and (continuous) target $y$. I want to train a polynomial regression model with lasso penalty, in order to rank features by importance by ...
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Forecasting with unscaled data using coefficient estimates from a penalized regression on scaled data

This is an embarrassingly basic question, but I keep producing absurd estimates from a pretty well-fitting model, so I think I’m making some fundamental mistake. I have a longish time series that I am ...
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Scaling multi input LSTM

I have a single layer LSTM model with 300 time series which try to predict the next value for one time series, based on past 12 values of the 300 time series. 56 is the number of slices of length 12 ...
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Min-Max Scaler for fixed effects regression?

I m currently doing some social science research using panel data to determine the impact of budget cuts on financial vulnerability. I have decided to use a fixed effects model to determine this ...
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Scaling dataset in Random Forest

Scaling a dataset for Random Forest modelling is not necessary. However, if we have already done the scaling and normalization to the dataset, will it impact our Random Forest modelling?
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Is it okay to re-scale values that were standardized before some rows were excluded?

The data I was given is scaled to have mean = 0, sd = 1 (no, I do not have the original data, and no I cannot get it). After receiving the data I excluded half the rows, so obviously the resulting ...
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How do I scale my input so that outliers aren't throwing off the data?

I've scaled my inputs between -1 and 1 but outliers are still throwing my data off. i.e. 99% of the range is occupied by one outlier and the rest of the data sits between -0.001 and 0.001. I see that ...
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1 vote
1 answer
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What data sets should be used when calculating scalers?

When doing maching learning tasks, it is common to divide the whold data set into three unoverlapping subsets, namely training set, validation set and test set. I understand that the test set should ...
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Normalizing/Scaling a dataset does not have any effect on r2 score?

I have this question on my mind for some time now, but unable to find some thorough explanation around this. While working on the Boston housing data set, scaling the data has no effect on the output. ...
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Scaling a gene expression data generates NA values

I would like to analyze the prostate gene expression data which has a link named 12859_2005_967_MOESM4_ESM.tgz in the site here. In a paper I read, the author scaled the predictors in the training set ...
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Neural network training better without MinMaxScaler()?

Forum, I have a multivariate time series problem; For my master thesis I am investigating whether it is possible to forecast the movement direction of stock price with machine learning. My model looks ...
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Scaled test data value range is much different than the scaled train data

I am trying to create an LSTM model for time series prediction. I am using MinMaxScaler (from the library sklearn for python) for scaling the data. At first, I didn't know that you shouldn't fit the ...
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Why are my weights and and bias incorrect after scaling?

I am trying to create my own linear regression model in Python. I have a working model, but when I try to add a preprocessing function that scales the feature vectors I get incorrect weights. Below is ...
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