Feature Engineering

This step helps to extract more information from existing data. New information is extracted in terms of new features. These features may have a higher ability to explain the variance in the training data. Thus, giving improved model accuracy.

Feature engineering is highly influenced by hypotheses generation. A good hypothesis results in a good feature. That’s why experts always suggest investing quality time in hypothesis generation. Feature engineering process can be divided into two steps:

Feature Transformation
Feature Creation

Feature Transformation:

There are various scenarios where feature transformation is required: * Changing the scale of a variable from original scale to scale between zero and one. * Some algorithms works well with normally distributed data. Therefore, we must remove skewness of variable(s). There are methods like log, square root or inverse of the values to remove skewness * Binning of numerical variables

Feature Creation:

Deriving new variable(s) from existing variables is known as feature creation. It helps to unleash the hidden relationship of a data set. Let’s say, we want to predict the number of transactions in a store based on transaction dates. Here transaction dates may not have a direct correlation with the number of transaction, but if we look at the day of a week, it may have a higher correlation. In this case, the information about the day of the week is hidden. We need to extract it to make the model better.

Creating a variable based on a mathematical computation on three existing variables is a method of?

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Feature Transformation
Feature Creation
Feature Selection

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Exercise

Feature Engineering

Feature Transformation:

Feature Creation:

Creating a variable based on a mathematical computation on three existing variables is a method of?

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