AI is a broad spectrum that covers a wide range of topics, such as neural networks, expert systems, robotics, fuzzy logic, and more. ML is a subset of AI. It explores the idea of building a machine that learns on its own, thus surpassing the need for constant speculation. Therefore, ML has led to a major breakthrough for achieving AI.

ML incorporates the use of several algorithms, thus allowing software to provide accurate results. Making a useful prediction from a set of parsed data is what the concept of ML aims to do. The foremost benefit of ML is that it can tirelessly learn and predict without the need for a hardcoded software regime. Training includes feeding huge datasets as input. This allows an algorithm to learn, process, and make predictions, which are provided as output.

Several important parameters are employed when measuring the potential of any model. Accuracy is one of them, and is an important parameter in measuring the success of any developed model. In ML, 80% accuracy is a success. If the model has 80% accuracy, then we are saving 80% of our time and increasing productivity. However, it is not always the best metric for accessing classification models if the data is unbalanced.

In general, accuracy is termed as an intuitive measure. While employing accuracy, equal cost is assigned to false positives and false negatives. For imbalanced data (such as 94% falling in one instance and 6% in other), there are many great ways to decrease the cost; make a vague prediction that every instance belongs to the majority class, prove that the overall accuracy is 94%, and complete the task. In the same line, problems arise if what we are talking about, such as a disease, is rare and lethal. The cost of failing to properly examine the disease of a sick person is higher than the cost of pushing a healthy individual to more tests.

All in all, there are no best metrics. It is common for two people to choose different metrics to reach their goal.