Fundamental Techniques of Feature Engineering for Machine Learning
https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114
IntroductionWhat is a feature and why we need the engineering of it? Basically, all machine learning algorithms use some input data to create outputs. This input data comprise features, which are usually in the form of structured columns. Algorithms require features with some specific characteristic to work properly. Here, the need for feature engineering arises. I think feature engineering efforts mainly have two goals:
According to a survey in Forbes, data scientists spend 80% of their time on data preparation:
Fundamental Techniques of Feature Engineering for Machine Learning
Apr 1, 2019, 2:20pm UTC
https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114
> IntroductionWhat is a feature and why we need the engineering of it? Basically, all machine learning algorithms use some input data to create outputs. This input data comprise features, which are usually in the form of structured columns. Algorithms require features with some specific characteristic to work properly. Here, the need for feature engineering arises. I think feature engineering efforts mainly have two goals:
> According to a survey in Forbes, data scientists spend 80% of their time on data preparation: