Machine Learning development has been in high demand in recent years, trending with the millennials and accelerating in other generations, too. It requires hours of learning, testing, developing, and monitoring the processes daily. Undeniably, there are moments when users ask themselves how good their model actually is. Model performance evaluation is a crucial part of processing because it has a huge effect on Machine Learning prediction accuracy, which is the main characteristic and service that attracts individuals and companies to use it. High-performance models are always in demand because of their trustworthy and valuable data outcomes that can be upgraded and used in future model(s) development. High performance models and ordinary models are divided by accuracy, that can be defined as an evaluation metric for classification assignments. It’s one of the well-known and most popular validation methods in the Machine learning environment. An accuracy formula in Machine Learning (A.K.A., accuracy equation in ML). In binary format, it is:
T P + T N * T P + T N + F P + F N ( True Negative + True Negative * True Positive + True Negative + False Positive + False Negative).
*For an in depth discussions, search the Confusion Matrix formula*
There are also a few more metrics used to evaluate the model such as Recall, Precision, and F-Score. Feel free to research and investigate before creating your model preferences. You should also determine the valuable metrics for your model processing and output score. Industry standards are between 70% and 90%. Everything above 70% is acceptable as a realistic and valuable model data output. It is important for a models’ data output to be realistic since that data can later be incorporated into models used for various businesses and sectors’ needs.