Feature Scaling Using R Programming Language

In R It essentially involves taking an input variable and scaling it down so that its mean value is 0 (or close enough). This will make your model more stable, which can improve its performance – you’ll get better predictions without having to train the model for longer than necessary.

It’s important to note that feature scaling does not come for free: you have to carefully choose which features should be scaled down and when they should be scaled down (and why).

Feature scaling is a technique to improve the accuracy of machine learning models. This can be done by removing unreliable data points from the training set so that the model can learn useful information about relevant features. Feature scaling is widely used in many fields, including business analytics and clinical data science.