Leave-One-Out Cross-Validation in Machine Learning

Automatically adapting new productive approaches from the previous effort is one of the traditional ways of improving upcoming challenges. Machine learning (ML) is very popular for the same reason. Machine learning also learns from past experiences using past data. The important mechanism is automation in all procedures. On the basis of historical data, machine learning will be able to predict related outcomes by various mathematical models on data-driven approached. There are many types of algorithms to support the ML framework. Model efficiency plays an important role for better outcomes. Leave-one-out cross-validation is one of the techniques for checking the efficiency. The processes are executing N times for deriving better outcomes.