1) Name a several linear and non linear classifiers. Using a linear model that perfectly separates a set of data points with two labels is not always a good idea. Why is that? Give an example. What methods can you use to mitigate this 2) what is information leak in predictive modeling? Are leaks really a problem? Give an example. 3)When we fix a parameterized numeric model to data, we try to find the optimal model parameters. What does optimality mean? Optimal to what? How is this optimality relevant to data mining machine learning? 4)What does it mean for one attribute to give info about another attribute? Give an example of how one would find an attribute that gives info about another attribute? How do you apply this property between attributes to develop your machine learning models