From the course: Machine Learning and AI Foundations: Value Estimations
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Feature engineering
From the course: Machine Learning and AI Foundations: Value Estimations
Feature engineering
- [Instructor] When using supervised learning to solve a problem, we feed features into a machine learning algorithm, and the algorithm learns how to predict the correct output based on those features. This is a simple idea, but in real-life applications it can take a lot of trial and error to figure out which features are most useful for modeling the problem. Feature engineering is using our knowledge of the problem to choose features or create new features that allow machine learning algorithms to work more accurately. Feature engineering will consume the majority of your time when you are building supervised learning systems. Doing a good job of feature engineering will make a large difference in the quality of your model. To get the best result possible when training a machine learning algorithm, we want to make the problem as simple as possible for the algorithm to model. That means we want to feed in features that correlate strongly with the output value. In fact, including…
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