Feature Engineering: summary of common feature transformation methods

The life cycle of machine learning model can be divided into the following steps ：

• Data collection

• Data preprocessing

• Feature Engineering

• feature selection

• Building models

• Super parameter adjustment

• Model deployment

To build a model, we must preprocess the data . Feature transformation is one of the most important tasks in this process . In the data set , Most of the time, there will be data of different sizes . In order to make better prediction , Different features must be reduced to the same amplitude range or some specific data distribution .

There are many methods of feature transformation , This article will summarize some useful and popular methods .

• Standardization

• Min — Max Scaling/ Normalization

• Robust Scaler

• Logarithmic Transformation

• Reciprocal Transformation

• Square Root Translation

• Box Cox Transformation