Sparse Attention Regression Network Based Soil Fertility Prediction With Ummaso

arXiv:2404.10274v1 Announce Type: cross
Abstract: The challenge of imbalanced soil nutrient datasets significantly hampers accurate predictions of soil fertility. To tackle this, a new method is suggested in this research, combining Uniform Manifold Approximation and Projection (UMAP) with Least Absolute Shrinkage and Selection Operator (LASSO). The main aim is to counter the impact of uneven data distribution and improve soil fertility models' predictive precision. The model introduced uses Sparse Attention Regression, effectively incorporating pertinent features from the imbalanced dataset. UMAP …

abstract aim approximation arxiv attention challenge cs.ai cs.lg datasets lasso least manifold network prediction predictions projection regression research shrinkage type umap uniform