Data Science

Previously, in the post Loss Functions in Machine Learning and LTR we disscussed about how loss functions were used in ML and briefly mentioned LTR. Here I’ll discuss about LTR.

Previously, decision theory was disscussed and an important part of is to evaluate a decision rule for decision making. Since the risk is the average loss \(L(\theta,d)\), different loss functions were used in Machine Learning models.

Statistics starts with probability theory, particularly in the analysis of games of chance. To be refferred to as a game, it involves three elements mathmatically: Parameter space \(\Theta\), a population characteristics, a physical quantity for example, mean.

There are so many terms regarding the field of Statistics and Data Science. We often heard Statistics, Data Mining, Machine Learning, Big Data, etc. It especially confuses people that’s in a different field.