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  3. Introduction to Python & Machine Learning (with Analytics Vidhya Hackathons)

Exercise

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?

Instructions

50 XP

Possible answers