Machine learning, a division of artificial intelligence (AI), is a data analysis technique that focuses on automating the construction of analytical models. It operates on the principle that systems can learn from data provided to them, identify patterns and make decisions, reducing the need for human intervention [1]. Machine learning encompasses two main techniques: supervised learning, which aims to predict a specific target variable, and unsupervised learning, which aims to identify patterns or groups within the data without a predefined objective [2].

In the realm of machine learning, achieving accuracy is of utmost importance. When developing a model, the primary goal is to maximize its accuracy. However, it is important to acknowledge that building a model that is 100% accurate is unattainable since errors are inherent [3]. Understanding the sources of errors, which contribute to bias and variance, can greatly improve the process of data adjustment, leading to more accurate models. Bias refers to the extent to which the predicted values deviate from the actual values. High bias occurs when the average predicted values significantly differ from the actual values. On the other hand, variance arises when a model performs well on the trained dataset but poorly on unseen data. Models with high variance heavily rely on the specific sample used for prediction, impacting the quality of the predictions.

In the pursuit of accurate predictions, machine learning focuses on rare events. Evaluating the accuracy of machine learning models involves two essential metrics: precision and recall. Precision measures the fraction of relevant instances correctly identified among all the instances obtained [4]. Recall, on the other hand, calculates the fraction of relevant instances identified compared to the total number of relevant instances. Both precision and recall rely on an understanding and assessment of relevance.

References:

[1] R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, "Machine Learning: An Artificial Intelligence Approach," Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

[2] R. S. Sutton and A. G. Barto, "Reinforcement Learning: An Introduction," Cambridge, Massachusetts: MIT Press, 1998.

[3] P. Langley, "Elements of Machine Learning," San Francisco: Morgan Kaufmann, 1996.

[4] P. Mars, "Learning Algorithms: Theory and Applications in Signal Processing, Control, and Communications," Milton: CRC Press, 2018.