Feature Scaling Demystified—Essential Linear Scaling Techniques in Machine Learning!

Author(s): Harshit Dawar

Originally published on Towards AI.

Let’s understand the most useful linear feature scaling techniques of Machine Learning (ML) in detail!

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Machine Learning (ML) is a very vast field & requires a proper approach to formulate the solution for every problem, irrespective of the solution or problem being small scale or large scale.

Since all ML models expect numeric input, it doesn’t signify that passing the numeric features as they are fulfills the use case. Though the model will be trained, but the quality of the model is still the question.

Many people who admire being an ML engineer or even existing ML engineers just send the data as it is (without the required processing) to the model for its training, without knowing that its not the optimized way. Its more like a scenario where you are communicating with a person in Hindi who only understands English, though the person can somehow try to understand what you are saying with the help of a translator, but at the end, its not effective. To make it effective, you must communicate with that person in English only.

So, instead of following the group of people who are just trying to climb a big ladder with one step, a proper… Read the full blog for free on Medium.

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Published via Towards AI