Feature engineering
Machine learning models are trained using input data to later provide as an outcome a prediction on unseen data. This input data is regularly composed of features that usually come in the form of structured columns. The algorithms use this data in order to infer patterns that may be used to infer the result. Here, the need for feature engineering arises with two main goals, as follows:
- Refactoring the input data to make it compatible with the machine learning algorithm we have selected for the task. For example, we need to encode categorical values if these are not supported by the algorithm we choose for our model.
- Improving the predictions produced by the models according to the performance metric we have selected for the problem at hand.
With feature engineering, we extract relevant features from the raw input data to be able to accurately represent it according to the way in which the problem to be solved has been modeled, resulting in an...
