MSE: 0.002
Papers with Code · CC BY-SA 4.0
Feature Engineering
Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.
The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.
개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.
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데이터셋
14개논문
MAE (10% missing): 0.219
1 step MAE: 1.76
MAE (10% missing): 0.248
MAE (10% missing): 0.363
Test error: 0.144 ± 0.06
MAE (10% missing): 0.477
MAE (100 steps, 10% data missing): 0.735
14 gestures accuracy: 0.98
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