Forensic Prediction using Bias-Variance Tradeoff and Random Forest Algorithms

Abstract

Every individual host after death has its own altered micro biome configuration. After death, postmortem microorganism communities change to represent the attributes of death. The micro biome act as a many roles in human health, usually done by the exclusive lens of clinical interest. By scouring 5 anatomical areas throughout regular demise exploration from 188 case to predict the Postmortem Interval (PMI), location of death and manner of death, the postmortem micro biomes were collected. The micro biome sequencing are not easy to analyze and interpret because it produces large multidimensional dataset. To overcome the analytical challenge Machine learning method can be used. The two supervised machine learning methods employed here are Random Forest and Bias-Variance Tradeoff. In training datasets, Random forest algorithm is applied. This algorithm makes predictions by choosing the most voted node from each decision tree as the output. The output is checked for any bias variance error, by the Bias-Variance Tradeoff algorithm in order to help the supervised learning algorithm to perform generalization beyond the training datasets. To obtain a prediction that is best fitted and accurate, these two algorithms are chosen for learning.