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
Discuss Discretization in the context of feature engineering
Discretization refers to the process of binning a continuous variable into a discrete number of buckets.
Discuss Timestamp Date Extraction in the context of feature engineering
Timestamp attributes can be very informative for certain forecasting applications.
Discuss text feature extraction in the context of feature engineering
In some text fields, especially those that follow a consistent pattern throughout all of the observations, features can be extracted based on sub-components of the original text strings.
Discuss Ordinal encoding in the context of feature engineering
This is another way to convert a categorical variable into a numeric representation suitable for model training.
Discuss Dummy encoding in the context of feature engineering
In order to represent a categorical variable in a machine learning model, it usually must be somehow coded numerically before it is used in the training of a model.
What are some common feature engineering techniques?
Some of the common feature engineering techniques are Dummy Encoding, Ordinal Encoding, Text Feature Extraction, Data Extraction, and Discretization
What is Feature Engineering?
Feature engineering is the process of deriving informative input attributes that can be passed to a machine learning algorithm in order to learn their associations with the target variable.