Fig. 4: illustrates the machine learning workflow employed to assess the potential of a biochar-mediated aqueous system for pesticide treatment.
From: Using machine learning to predict the efficiency of biochar in pesticide remediation
The first block (i) represents the compilation of pesticide adsorption data, i.e., biochar’s textural properties, water matrix parameters, experimental factors, and adsorption capacity, followed by data pre-processing for model development. The second block (ii) outlines the steps involved in the model training process, where a five-fold cross-validation methodology was implemented to address overfitting concerns. The third block (iii) pertains to model performance evaluation using the test data and the utilization of the best predictive model for feature exploration.
