Questions tagged [feature-scaling]

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.

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why do we need re-scaling ? Is it necessary for all types of data including interval and frequencies

Re-scaling seems to have become a fashionable technique. The methods have propped up to deal with several types of data. Moreover,it is applied without much of context. Should we use it for only ...
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60 views

What is the difference between normalization and re-scaling?

This site does not describe the nature of the normalization tag. How does it differ from re-scaling? Many authors use the two terms interchangeably. I can not understand normalization's operational ...
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How important is outcome variable scaling in SVM regression?

Should I scale outcome variable for SVM regression? What is the magnitude of impact of outcome variable scaling in SVM regression?
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23 views

Feature scaling for clustering

I want to cluster groups, using K-Means, DBSCAN, etc. algorithms, based on lat-lng coordinates along with other features such as dummy variables, continues variables (in different units). What would ...
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Data scaling for convolutional neural networks and other issues

My project involves deep learning on physiological data gathered from a wearable device. I aim to evaluate the potential CNN usefulness in classifying data collected from the wearable. The wearable ...
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26 views

Which PreProcessing method should be used?

I have a dataset that consists of a poisson distribution, a exponential distribution, categorical variables, and my target variable is a numerical bimodal variable. This is a regression model. I ...
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What are good feature selection and engineering approaches for data with known uncertainties?

Context: I am working with a set of geological features that could have uncertainty values attached to them (for example, values come from drill holes that are sparsely distributed and must be ...
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estimate user's satisfaction of a video based on how much of it they watched - normalization

I am trying to estimate how much a user liked a video using how much of the video they watched. Let's say, on the scale of 1 to 10, 1 means that the user didn't like it at all, and 10 means they ...
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Can a term weighting function used in text retrieval be compared to one used in text classification?

I came up with a modified version of TF-IDF function for text retrieval task. I want to do retrieval experiments using Vector Space Model and compare my function to some of those proposed in the ...
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How best to use the resale transaction year in predicting housing prices?

I'm looking into the classic problem of predicting apartment prices (resale market) based on the their type, size, location, etc. Pretty straightforward and Linear Regression or Regression Trees give ...
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How to scale features for LSTM?

I am trying to correctly scale data for LSTM. I have a set of features like this: x1(t-1), ... xn,(t-1), y(t-1), where the ...
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Scalling features for competition participants

Hello there and Happy Holidays. I have a data set with each row representing a race with 6 participants, with each participant having its own column for each feature. The target variable is ...
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StandardScaler's mean and standard deviation for real-life data?

I've heard that we should use train dataset's scale for that of test data so they are in line with each other in terms of scale. And I know we use transform() ...
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Converting the continuous numerical features into gaussian distribution and how to deal with NaN values after that?

I have a dataset in which there are few continuous numerical features that distribution over them is non gaussian and this means, skewness is nonzero (positive or negative). I read that it is good to ...
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53 views

K-Fold cross validation and data leakage

I want to do K-Fold cross validation and also I want to do normalization or feature scaling for each fold. So let's say we have k folds. At each step we take one fold as validation set and the ...
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21 views

Scaling the activation function

It is obvious that I have to scale the output data if the range of values is between say [-10;10] and the activation function of the output layer takes values in the interval [-1;1]. But I could also ...
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Using MinMaxScaler on Training Set… Do I need to scale the input for a prediction as well?

I know this is a rookie question, but I'm having trouble with getting predictions out of a model. I use a MinMaxScaler() function on the training set as seen below... ...
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Scaling the data iteratively one by one vs batch scaling

I have 2000 signals in a dataset of shape (2000, 400000) where each signal is recorded within the range -127, 128. I want to downscale each signal from (-127, 128) ...
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Is feature scaling at all needed for a feature set with a single feature?

I understand that feature scaling is required to bring features in different magnitudes on a common scale so the model is not biased towards features with higher magnitudes. But if there is only a ...
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53 views

How to choose between different types of feature scaling?

The feature set for my multi-class multi-label classification task, using the MLPClassifier from scikit learn, contains mostly features where the values are in the same range of [0,1], but there are 3 ...
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Preprocessing and feature selection in group k fold

I have experimental data collected from 10 people. From each person, 100 data points were collected under condition A, and 100 data points were collected under condition B. So, in total I have 10*(100+...
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Scaling continuous data to discrete range [closed]

Edit: the context is as follows: I've trained some ML model that predicts some feature vector. Thats a. But I know that a can take some values from discrete range, ...
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Predict real world data after modelling with scaled features [duplicate]

I trained and test a model with scaled features. Now, I want to predict a single real world sample. If I have one sample alone, I can't scale it to fit into the model like I did with the test data. I ...
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29 views

difference between scaling/normalizing data at a specific step

I am using the MinMaxScaler normalization method, however I have seen various ways that this can be done, I want to know if there is any actual difference between the following: 1. Standardizing/...
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How to get a KNN model (using quantiles to scale variables due to non-normal distributed data) to be better suited for non-extreme values in the data?

I want to cluster my data via k-means/modes. As the variables in my data are not normal distributed, I am not using the z-transformation to scale my data. I am scaling my data by categorizing each ...
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67 views

Should one-hot encoded categorical features needs to be scaled when used along with text feature while deriving semantic similarity?

My aim is to derive textual similarity using multiple features. Some of the features are textual for which I am using (Tfhub 2.0) Universal Sentence encoder. There are other categorical features which ...
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Generalize min-max scaling to vectors

I am combining several vectors, where each vector is a certain kind of embedding of some object. Since each embedding is very different (some have all components between $[0, 1]$ some have components ...
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57 views

SVR - RMSE is much worse after normalizing the data

I'm building a model using a custom kernel SVR that looks into a few of my dataframe's features and checks the proximity/distance between each pair of datapoints. The features are weigthed and the ...
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Recommended Tutorial Videos or Books on Feature Engineering Using Python [duplicate]

I will appreciate it if you guys can recommend for me a good hands-on tutorial videos or books on feature engineering using Python. I do not want videos or books that teach only the theory behind ...
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Data scaling for training and test sets

when we are scaling the data i needed some clarification. so for preventing data leakage we split the train and test sets and then perform the scaling on them separately, correct? so while scaling or ...
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137 views

Shall I use ordinal encoding or One-Hot-Encoding when using DBSCAN for content clustering on websites?

I want to cluster the preparation steps on cooking recipes websites in one cluster so I can distinguish them from the rest of the website. To achieve this I extracted for each text node of the website ...
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43 views

Normalize data with extreme outliers for forecasting

Suppose I have input values that represent the change of a stock share from each time step to the next. Now I want to feed these values into an LSTM Neural Net. My problem is that most values are ...
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224 views

How do standardization and normalization impact the coefficients of linear models?

One benefit of creating a linear model is that you can look at the coefficients the model learns and interpret them. For example, you can see which features have the most predictive power and which do ...
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Is it a good practice to evaluate model performance by comparing the metrics of rescaled (inverse transformed) predictions and true target values?

I am now working with a Linear Regression for a time-series regression problem (I am sorry that I cannot say too much about the problem and feature vector due to NDA). I scaled both the input values ...
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Is there a common relationship between data inputs and the number of attainable features?

Is there a known relationship between the amount of information gain that comes from new data added to a dataset? for eg: If I have a plant watering system that tells me: An integer of how wet the ...
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How to combine two differently scaled, but equally important “running” signals into a reward function?

I asked this question on Artificial Intelligence, but got no answer, so I am moving it here. I have two signals that I want to use to model a reward for a reinforcement learning algorithm. The first ...
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A question on pipeline

I made a pipeline with standard scaler and k means .When I fit the pipeline to the training data, Does the standard scaler just fits or fits and transforms the training data?
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NotFittedError says this StandardScaler instance is not fitted yet while using inverse_transform() [closed]

I have a dataset and i have used Support Vector Regression.So i needed to use StandardScaler module from sklearn.preprocessing fro Feature Scaling. After training my model when i came to predict it ...
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50 views

Using word embeddings with additional features

I have the set of queries for classification task using Gradient Boosting Classifier of scikit learn. I want to enrich the model by feeding additional features along with GloVe. How should I approach ...
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Does the test set has to be in [0,1] range?

I have standardized training set using mean = XTrain.mean() XTrain-=mean std = XTrain.std() XTrain/=std And then used mean and ...
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71 views

How to perform a running (moving) standardization for feature scaling of a growing dataset?

Let's say that there is a function $r$ $r_n = r(\tau_n)$, where $n$ denotes a so-called time-step of a system with an evolving state. Both $\rho$ and $\tau$ should equally influence $r$, and should ...
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320 views

feature scaling xgbRegressor

I read for example in this answer: Does the performance of GBM methods profit from feature scaling? that scaling doesn´t affect the performance of any tree-based method, not for lightgbm,xgboost,...
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184 views

Does the performance of GBM methods profit from feature scaling?

I know that feature scaling is an important pre-processing step for creating artificial neural network models. But what about Gradient Boosting Machines, such as LightGBM, XGBoost or CatBoost? Does ...
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21 views

Should I scale high ranging ordinal fields?

In the left column, I have an ordinal integer field. In the right column, I have a scaled float feature. Should I scale the ordinal field since it is getting so much bigger than the other feature?
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324 views

what is difference between fit and fit_transform in sklearn while applying feature scaling [duplicate]

I have seen few post related to this question but i am not quite clear about my confusions as mention bellow. I have some confusion related to fit and fit_transform. suppose, I have X_train and X_test ...
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47 views

Data scaling for large dynamic range in neural networks

The usual strategy in neural networks today is to use min-max scaling to scale the input feature vector from 0 to 1. I want to know if the same principle holds true if our inputs have a large dynamic ...
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Understanding Scaling With Multiple Datatypes in a Neural Network

I've looked around and seen a huge amount of discussion on scaling inputs and targets for neural networks, but can't seem to find universal agreement on a few issues. Suppose I had a dataset, with ...
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176 views

When do we scale features and should it be done to label encoded features?

So, I am trying to make a linear regression model for predicting car prices for which I have the following data set: Data Set Since the unique values are a lot for the categorical features, I label ...
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67 views

How to work with Log-transformation?

I'm beginning my data science journey and I've faced a challenge that confuses me a bit. I have a set with few features and a target variable whose raw distribution is highly skewed. I've read that ...
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284 views

What would be the mse (mean squared error) of my scaled dataset on the original scale?

I build an LSTM model on a standardized dataset using sklearn's MinMaxScaler. All values of the dataset are between 0 and 1. Features and target variables were standardized between 0 and 1. I achieve ...