Data, Analytics, and Learning

Competency 6.1: Feature Engineering

Features engineering is an art of creating predictor variables and is the least well –studied part of the process of developing prediction models. It’s clear in feature engineering that models will never be good if their predictions aren't any good.

Some processes of feature engineering are:

1.       Brainstorming features

2.       Deciding what features to create

3.       creating the feature

4.       Studying the impact of features on model goodness

5.       Iterating on features if useful

Competency 6.2: Diagnostic Metrics  

There are various types of Diagnostic metric tools out there, Roc which stands for Receiver- Operator Characteristic Curve.  With Roc, one can predict something which has two values such as

1.       Correct/Incorrect

2.       Gaming the system/not gaming the system

3.       Student dropouts/ Not drop out

Using Roc, prediction models can output probability or even a real value.