What is the difference between Standard Scaler and Min-Max Scaler?

Standard Scaler centers data around a mean of 0 with a standard deviation of 1, while Min-Max Scaler rescales data to a fixed range, typically between 0 and 1.

Can I reverse the scaling process later?

Yes, if you enable 'Save Scaling Parameters', the tool provides the necessary JSON parameters to perform an inverse transform on future data.

What happens if I don't specify columns to scale?

The tool will automatically detect and apply the selected scaling method to all numeric columns found in your CSV input.

Which scaling method should I use for data with outliers?

Robust Scaler is recommended for datasets containing significant outliers, as it uses the median and interquartile range rather than the mean and variance.

Is my data stored on your servers?

No, all data processing is performed locally in your browser to ensure your sensitive information remains private.

Elysia Tools

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Data Processing

Feature Scaler

Scale and normalize features using various methods for machine learning preprocessing and data standardization

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Inputs

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9 options

ContentPaste or type the main input values.3

CSV Data InputtextareaRequiredColumns to Scale (optional)textOptionalSpecify which columns to scale. If empty, all numeric columns will be automatically detected and scaled.Scaling Parameters (for inverse transform)textareaOptionalOnly required for inverse transform. Paste the scaling parameters from a previous run.

SettingsAdjust formats, ranges, numbers, and modes.2

Scaling MethodselectRequiredOutput FormatselectRequired

TogglesEnable or disable optional behavior.4

First Row Contains HeaderscheckboxOptionalEnabled when checkedTreat first row as column headersInverse TransformcheckboxOptionalEnabled when checkedApply inverse transformation using provided parametersSave Scaling ParameterscheckboxOptionalEnabled when checkedInclude scaling parameters in the output for future inverse transformationsOutput Only Scaled DatacheckboxOptionalEnabled when checkedSkip detailed analysis and only output the scaled data

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Feature Scaler

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1. Normalizing Sensor Data for ML

2. Standardizing Financial Features

FAQ

CSV Samples

Python Samples

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