Artificial Intelligence and Machine Learning Foundations: Learning from experience by Andrew Lowe

Author:Andrew Lowe [Lowe, Andrew]
Language: eng
Format: epub
Published: 0101-01-01T00:00:00+00:00

Logistic regression – this is a mathematical model used in statistics and ML to estimate the probability of an event occurring having been given some previous data. Logistic regression works with data where either the event variable (y) happens (1), or the event does not happen (0). An example could be around credit card transaction approval when multiple inputs such as time of purchase, place of purchase and type of purchase may determine if the transaction is approved or not.

Decision tree – this is one of the most common ML algorithms in use today. It is a supervised learning algorithm used for classifying problems by moving down a tree root from node to node testing an attribute at each node. It is typically used in expert or smart systems: it can advise a course of action to be taken based on previous successful actions, and is typically seen as a series of questions and suggestions culminating in a ‘was this information useful?’ question on self-help computer systems. An example could be the fault light on a non-functioning printer: is it red or green? If green, then check if there is paper in the input tray, if the answer is yes, then check for a paper jam and so on, until the problem is identified. The learning element of the algorithm can weight suggestions based on previous successes with particular attributes. Equally, decision trees can be used in association to classify patients and likely disease based on health questions, or loan applications and previous credit history.

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