For my studies on economy, I work on prediction of judicial decisions. I don't really understand the interactions between several concepts:
- predictive analytics
- machine learning
- case-based reasoning
- rules-based reasoning
At the moment, I understand that predictive analytics is an area of statistics that deals with extracting information from data and using it to predict a outcome, like a judicial decision. In predictive analytics there are 2 kinds of tools: econometric tools like MCO regression and machine learning tools like neural networks.
Among ML tools, we can distinguish between 2 approaches: rules-based reasoning and case-based reasoning. Given a given legal problem, with the rules approach, the algorithm learns the rules that led to these data, deduce a linear model and then we can confront the problem to the model to deduce the solution given by a court. With cases approach, the algorithm brings the given problem closer to similar cases found in the data, and deduces a solution given by a court.
Is it correct?
Are different kind of ML tools like K-NN or neural network only used for one type of reasoning? or they can be used for both?