If I explore all the algorithms and tools necessary for learning from data (training a model with data) and being capable of predicting a numeric estimate (for example house pricing) or a class (for instance the species of an iris flower) given any new example that I didn’t have before. If I start with the simplest algorithms and work toward those that are more complex. The four algorithms represent a good starting point for any data scientist.
Regression has a long history in statistics from building simple but effective linear models of economic, psychological, social or political data, to hypothesis testing for understanding group differences, to modeling more complex problems with ordinal values, binary and multiple classes, count data, and hierarchical relationships, it is also a common tool in data science, a swiss army knife for machine learning that I can use for every problem. Stripped of most of its statistical properties, data science practitioners perceive linear regression as a simple, and an understandable, yet effective algorithm for estimations and in its logistic-regression version, for classification as well.
I would like to know about the simplest algorithm, as a tool in data science for machine learning and linear regression as a simple and understandable, yet effective algorithm for estimations. , if possible in its logistic-regression version, for classification as well.