In the vector unit length scaling, a feature vector is divided by the Manhattan distance (l1 norm) or by the Euclidean distance (l2 norm). We can use the Normalizer class from the sklearn.preprocessing module to perform vector unit length scaling. Let’s look at an...

What is Robust Scaler? Let’s say there is a numerical column in a dataset. And there are outliers in the column. In that case, we can use a robust scaler to scale the features. Robust scaler uses statistics that are robust to outliers. In the case of robust scaling,...

What is maximum absolute scaling in machine learning? In the maximum absolute scaling, each value of a numerical column is divided by the maximum value of the column. And the result is used to replace the original value of the column. In other words, $latex...

What is min-max scaling? As we discussed before, if a dataset has a large range of numerical values in one column and a small range of numerical values in another column, then the large range of values may dominate over the small range of values in certain machine...

What is standardization in machine learning? Standardization is a feature scaling technique in machine learning. Let’s say a dataset includes two columns – one for salary and one for age. Now, the salary column will store numbers in thousands and the age column will...