Mathematical Transformations in Feature Engineering: Log, Reciprocal, and Power Transforms Explained with Visualization

Author(s): Souradip Pal

Originally published on Towards AI.

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Imagine you’re preparing to bake a cake, but some ingredients are piled high, and others barely fill the spoon. Without smoothing out the proportions, your cake might turn into a disaster! This analogy works for machine learning models too. If your dataset has wildly varying scales and distributions, it’s like mixing unbalanced ingredients — your model won’t perform well.

In data science, the process of smoothing these “ingredients” is called normalization. Transformations like Log, Reciprocal, and Power Transforms, which we’ll discuss, help make your dataset more manageable, balanced, and ready for machine learning models to digest.

In this blog, we’ll explore why transformations are necessary, how to check if your data is normalized, and finally, how to visualize the impact of these transformations with Python libraries like QQPlot and distplot.

So, why go through the hassle of transforming your data in the first place? The short answer: to improve the accuracy and efficiency of your machine learning models. But let’s dig a little deeper.

In many real-world scenarios, data isn’t perfectly distributed. For example, income data tends to be heavily right-skewed, with many people earning modest amounts and a few… Read the full blog for free on Medium.

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Published via Towards AI