“At the end of the day, some machine learning projects succeed and some fail. What makes the difference? Easily the most important factor is the features used.”
-Dr. Pedro Domingos
What is feature engineering?
Feature engineering is the process of using domain knowledge to choose and transform the variables that will feed into your machine learning model. The process involves a combination of understanding the problem, data analysis, and applying good judgement. Feature Engineering is as much an art as it is a science.
Feature engineering happens prior to modeling, and is an essential part of building a machine learning solution. Appropriate well-designed features are often the deciding factor of the performance of your final algorithm. For this reason, data scientists often spend 70%-80% of their time on the pre-modelling phase, which largely consists of feature engineering.
Nairobi Traffic Jam Challenge
He explains the approach he took that helped him win the Nairobi Traffic Challenge and the target encoding technique he uses to transform categorical data into numerical variables. Here it is!
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