What is the difference between Feature Engineering and Feature Selection?
Feature Engineering is the process of using domain knowledge to extract numerical representations from raw data.
Feature Engineering is the process of using domain knowledge to extract numerical representations from raw data.
Categorical features are features that can take a limited, and usually a fixed number of possible values.
In the context of Machine Learning, ‘Sparsity’ is used to explain the degree of ‘emptiness’ of the data within a data structure.
Discretization refers to the process of binning a continuous variable into a discrete number of buckets.
This refers to a special case of discretization in which a continuous variable is transformed into a categorical representation that only consists of two bins.
Find out all the ways
that you can