feature engineering

A process that involves the following steps: 1. Determining which features might be useful in training a model. 2. Converting raw data from the dataset into efficient versions of those features. For example, you might determine that`temperature` might be a useful feature. Then, you might experiment with bucketing to optimize what the model can learn from different`temperature` ranges. Feature engineering is sometimes called feature extraction or featurization.

In TensorFlow, feature engineering often means converting raw log file entries to tf.Example protocol buffers. See also tf.Transform. --- See Numerical data: How a model ingests data using feature vectors in Machine Learning Crash Course for more information.

Practitioners refer to feature engineering when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.