ABSTRACT

Estimating flake mass based on remaining attributes bears an important relationship for the interpretation of lithic assemblages. Previous works have pointed out the relationship between flake attributes and prediction of flake mass. This study builds on previous works by using data from an experimental collection of flakes. Estimated mass was arrived at by generating a multiple linear regression model that combines several predictive variables. Variable selection for model training was carried out by using best subset selection, which evaluates all possible combinations of variables. Evaluation of the model was performed by computing common machine learning statistics along with estimated percentage error. Results make it possible to determine the best variables and estimate their relationships with flake mass. On the other hand, results also show that although the model is slightly biased and performs adequately, it has a limited inferential ability, especially when compared with other methods/indexes employed to estimate reduction.

摘要

基于剩余属性估计薄片质量与解释岩性组合具有重要关系。先前的工作指出了薄片属性与薄片质量预测之间的关系。本研究通过使用来自实验性薄片的数据收集了以前的作品。通过生成将多个预测变量组合在一起的多元线性回归模型得出估计质量。通过使用最佳子集选择来进行模型训练的变量选择，该子集选择会评估所有可能的变量组合。通过计算常见的机器学习统计信息以及估计的百分比误差来执行模型的评估。结果使确定最佳变量并估计其与薄片质量的关系成为可能。另一方面，