Questions tagged [feature-scaling]

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Scaling before autoencoder for anomaly detection

So I want to use autoencoder for binary classification (0 is normal, 1 is anomalous sample). I have training data which are artificially balanced and consists of both 0-s and 1-s, and testing data ...
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32 views

Look ahead bias induced by standardization of a time series?

Let's say I'm using some machine learning model to predict future values of a time series (e.g. stock price, air temperature, etc). In my model, I'm using some autoregressive features such as lagged ...
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Internal clustering criteria as a measure to know if feature transformation is useful

Does it make sense to use internal clustering criteria, such as Calinski-Harabasz, Davies-Bouldin and Silhouette, to draw conclusions about whether feature scaling makes sense with the existing data? ...
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2answers
55 views

Feature scaling dramatically improves performance

I am working with "Forest Coverage Type" Kaggle dataset (https://www.kaggle.com/c/forest-cover-type-prediction/data) and have applied support vector machine classification to predict forest coverage ...
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24 views

Feature Selection Before or after Encoding?

Should I apply feature Scaling and Selection before or after the One Hot Encoding/Label Encoding? Please Correct me if I'm Wrong- Deal with Outliers Impute ...
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18 views

dealing with imbalanced FEATURES

I am aware of the "class imbalance" problem... What I am asking here is using some "unbalanced features". Suppose we have a perfectly balanced class label for the dataset, but most of the features ...
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Can one normalize a PCA for specific features?

When dealing with data sets that have hundreds of dimensions, some phenotypic and some metadata, I would like to "normalize" the effect of specific (multiple) features on PCAs. I can get the ...
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49 views

which normalization should I use for my model based on time

I have a some measurements for the parking occupancy by the whole day (see picture). I want to predict the free parking spaces based on the measurements. I use for the prediction the SVR with RBF ...
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25 views

Missing Data imputation on one continuos column which depends on another feature and which does make sense only when such feature is positive

For each row (open contract) of my dataset, I have got a certain number of orders. I have created some features related to such orders; let's take for instance the average and the std deviation of the ...
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51 views

How does Feature Scaling help Gradient Descent? [duplicate]

I am following deep learning.ai's videos on Coursera. I have a couple of questions about feature scaling using the formula: $$ (x - \mu)/ \sigma $$ Edit: There are similar questions which deal with ...
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8 views

How To Feed Un-Scaled Data Into a Model That Was Trained on Scaled Data

I have a data frame that contains time series data. I split the dataframe into test and train. I want to prevent leakage so I split the data frame before doing any scaling. On the train data set, I ...
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1answer
50 views

Preventing information leakage when scaling a time series?

I have a time series $S_i$ that I want to train a regressor on to predict the next point in the time series. I want to split the data into training and validation sets, and also scale the data in the ...
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1answer
37 views

RMSE with and without standardizing the output variable

I have a time series data that I would like to be able to forecast. I was trying to standardize the data as my columns are all of different ranges. I have standardized the input variables, but was ...
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1answer
25 views

How to perform feature normalization for training regression CNNs with datasets with different distributions?

I'm teaching a 3D convolutional neural network to learn different functions that map a 3D scalar field into another one. It is essentially a regression problem. The distribution of input datasets ...
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39 views

Why does LASSO predict random data “well” during leave-one-out cross validation?

pre-amble: While investigating different cross validation strategies for small sample size dataset's with relatively large number of features I came across this peculiar result. While making a simple ...
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15 views

Why can't I feature scale after encoding categorical data

I've read somewhere that feature scaling categorical encodings (with vector mean/variance or median/IQR) is a bad idea and breaks the structure of the encoding - something about orthogonality of ...
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1answer
66 views

Why does feature scaling improves accuracy? [duplicate]

With feature scaling we just change representation of the data. This can make our model run faster but how this can improve accuracy? It is the same data after all. When I train my SVM without ...
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7 views

In what form should my features be in when running a model trained on features transformed through Weights of Evidence?

I have trained an anomaly detection model on features transformed/selected by means of the Weights of Evidence and Variance Inflation Factor approaches. My question is how should I go about running ...
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18 views

Feature Scaling/Standardization or Change Point Score?

I've different data sets that have the feature Volume. This feature represents the absolute number of events. Each observation represents a period (a fixed period such as 15 minutes). You can imagine ...
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Can we benefit some time without normalization of features? [duplicate]

There are several quetion in this forum about normalization of features in machine learning or regression. Almost all the answer points to the fact that one should normalize data. However, can there ...
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16 views

Adding dummy 0-valued observations to dataset when performing min-max scaling

I'm using min-max scaling on a dataset on which I'm training a regression model. I have an independent variable $a$ whose values in the training set range from, say $1000$ to $10000$ but in general, ...
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23 views

Effectively Standardizing Time Series Data

I'm currently taking an online course on Machine Learning with Time Series data, only the instructor proposes a calculation, ostensibly for centering and standardizing feature data, that seems way ...
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3 views

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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13 views

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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1answer
49 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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44 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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10 views

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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1answer
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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1answer
44 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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104 views

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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1answer
424 views

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 (...