Numerical transformations are essential techniques for preprocessing and manipulating data in order to enhance analysis and create meaningful insights.
These transformations are particularly useful when working with datasets containing different scales, units, or distributions. This guide will walk you through some of the most common numerical transformations, explaining their purposes and how they work.
Types of numerical transformations
Centering
Centering, or mean subtraction, is a simple transformation that involves subtracting the mean of a variable from all its values. This process shifts the mean of the variable to zero, but does not change its scale.
Centering is often used in conjunction with other transformations, like scaling, to improve the performance of machine learning algorithms.
Standard Scaler
The Standard Scaler is a transformation that not only centers your data but also scales it by dividing each value by the standard deviation. The result is a dataset with a mean of zero and a standard deviation of one.
This is also known as standardization or z-score normalization. Standardizing data can improve the performance of machine learning algorithms, especially those that are sensitive to the scale of input features, like support vector machines or k-means clustering.
Min and Max Scaler
The Min and Max Scaler is a transformation that rescales your data to a specific range, typically between 0 and 1. To accomplish this, the scaler subtracts the minimum value of a variable from each data point, then divides it by the range of the variable (maximum value minus minimum value).
Min-Max scaling is useful when you need to ensure that all input features have the same scale, which can be important for algorithms like neural networks.
Binning
Binning is a technique that involves converting continuous variables into discrete or categorical variables by grouping them into a predetermined number of bins. For example, you might group ages into categories like “0-9,” “10-19,” “20-29,” and so on.
Binning can be useful for simplifying data, reducing noise, or identifying trends and patterns in the data. It’s important to select the appropriate number of bins, as too few can lead to loss of information, while too many can result in overfitting.
Log Transformations
Log transformations are a type of power transformation that involve applying the natural logarithm (base e) or another logarithm (e.g., base 10) to each value in a variable. This transformation can help reduce the impact of outliers, correct skewness, and stabilize variance in your data.
Log transformations are particularly useful when dealing with variables that follow a power-law distribution or exhibit exponential growth, such as income or population data.
Conclusion
Numerical transformations are powerful tools for preprocessing and analyzing data. They can help improve the performance of machine learning algorithms, reveal patterns and trends, and make data more interpretable.
By understanding and applying these techniques, you can enhance your ability to work with and extract insights from complex datasets.
English bloke in Bangkok. First used GPT-3 in 2020 and has generated millions of words with it since. Not really much of an achievement but at least it demonstrates a smidgen of authority. Studies natural language processing, Python and Thai in his spare time.
