Ensemble methods, such as stacking, are designed to boost predictive accuracy by blending the predictions of multiple machine learning models. Recent work has shown that the use of meta-features, additional inputs describing each example in a dataset, can boost the performance of ensemble methods, but the greatest reported gains have come from nonlinear procedures requiring significant tuning and training time. Here, we present a linear technique, Feature-Weighted Linear Stacking (FWLS), that incorporates meta-features for improved accuracy while retaining the well-known virtues of linear regression regarding speed, stability, and interpretability. FWLS combines model predictions linearly using coefficients that are themselves linear functions of meta-features. This technique was a key facet of the solution of the second place team in the recently concluded Netflix Prize competition. Significant increases in accuracy over standard linear stacking is demonstrated on the Netflix Prize collaborative filtering dataset.

该研究提出了一种基于机器学习的线性技术 FWLS（Feature-Weighted Linear Stacking），该技术能够结合元特征来提高预测精度，保持速度，稳定性和可解释性，并在 Netflix 电影推荐比赛中取得第二名，相较于标准的线性堆叠方法显示出了显著的提高。

特征加权线性堆叠