Min-max normalization is one of the most popular ways to normalize data. For every feature, the minimum value of that feature gets transformed into a 0, the maximum value gets transformed into a 1, and every other value gets transformed into a value between 0 and 1. It is calculated by the following formula: v’…
The Z-score normalization is a popular and commonly used feature scaling technique. So let’s understand the feature scaling first and why it is required in the first place. Feature scaling It is one of the most important data preprocessing steps in machine learning. As different features in the dataset might have highly different ranges of…
Recently, I was implementing a Random Forest regressor when I faced the classical question: Should I implement data normalization? Before going into the depth of the topic, we will try to understand what normalization is. Normalization The goal of normalization is to change the values of numeric columns in the dataset to a common…
The term “normalization” usually refers to the terms standardization and scaling. While standardization typically aims to rescale the data to have a mean of 0 and a standard deviation of 1, scaling focuses on changing the range of the values of the dataset. As mentioned in [1] and in many other articles, data normalization…
