Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning

Computer Science

• Random Decision Forest 100%
• Learning System 100%
• Machine Learning 100%
• Machine Learning Algorithm 20%
• Class Classification 20%
• Impact Assessment 20%
• Predictive Accuracy 20%
• Prediction Performance 20%
• Training Data 20%
• Computation Time 20%
• Classification Task 20%