Prediction model of algal blooms using logistic regression and confusion matrix

Algal blooms data are collected and refined as experimental for algal prediction. Refined dataset is analyzed by logistic regression analysis, statistical tests regularization performed to find the marine environmental factors affecting blooms. The predicted value of bloom obtained through analysis using environment actual values applied confusion matrix. By improving decision boundary existing regression, accuracy, sensitivity precision prediction improved. In this paper, model established ensemble method improved, verified big analysis.