Feature engineering — это процесс выбора, преобразования или построения входных признаков, чтобы модель машинного обучения могла обучаться эффективнее. Он включает преобразование сырых данных в полезные представления, такие как feature vectors, buckets, encodings или derived variables.
Feature engineering is the process that involves defining a set of features and developing software that produces feature vectors from available phenomenon data, i.e., feature extraction. For more information, see the Feature engineering article on Wikipedia.
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 might be a useful feature. Then, you might experiment with bucketing to optimize what the model can learn from different 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.
Amazon Machine Learning: The machine learning process of constructing more predictive input representations or "features" from the raw input variables to optimize a machine learning model's ability to learn and generalize. Also known as data transformation or feature engineering.