Random Machine Learning #3

When Feature Scaling should be done? Feature Scaling should be done after splitting the data into train and test datasets. Feature scaling techniques such as normalization will be done using the train data. The important reason for normalization technique to be done after the train and test data split is to avoid data leakage. If

Random Machine Learning #2

What is Feature Scaling? During the data pre-processing stage, feature scaling is used to scale the independent variables within the same range (say between 0 and 1 or between -1 and +1). Why we need to do the feature scaling? This will ensure no independent variable dominates the machine learning algorithms. Do you know? Feature