PROJECTED VECTOR OVERFLOW PENALTY AS MITIGATION FOR MACHINE LEARNING MODEL STRING STUFFING

The invention relates to projected vector overflow penalty as mitigation for machine learning model string stuffing. An artifact is received from which features are extracted and used to populate a vector. The features in the vector are then reduced using a feature reduction operation to result in a modified vector having a plurality of buckets. Features within the buckets of the modified vector above a pre-determined projected bucket clipping threshold are then identified. Using the identified features, and overflow vector is then generated. The modified vector is then input into a classification model to generate a score. This score is adjusted based on the overflow vector and can then be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described. 本申请涉及作为机器学习模型字符串填充的抑制的投影向量溢出惩罚。接收工件，从工件提取特征并利用特征填充向量。然后使用特征简化操作对向量中的特征进行简化，以得到具有多个桶的经修改向量。然后标识出经修改向量中在预定投影桶裁剪阈值以上的的桶内的特征。使用所标识的特征，然后生成溢出向量。然后将经修改向量输入到分类模型中以生成得分。该得分基于溢出向量进行调整，然后可以被提供给消费应用或过程。还描述了相关的装置、系统、