Sparse modeling for small data: case studies in controlled synthesis of 2D materials
Yuri Haraguchi, Yasuhiko Igarashi, Hiroaki Imai, Yuya Oaki
Abstract
Straightforward, interpretable, and modifiable linear-regression prediction models with appropriate accuracy are constructed by sparse modeling coupled with our chemical perspectives as researchers on small data, such as experimental data in laboratories.
Topics & Concepts
Computer scienceData miningLinear regressionRegressionRegression analysisMachine learningMathematicsStatisticsMachine Learning in Materials ScienceElectronic and Structural Properties of OxidesMetal-Organic Frameworks: Synthesis and Applications