I am facing some confusion regarding the terminologies assocaiated to classification and regression problems esp. using the MLP and Perceptron models. These are the following:
1) When the data is linearly inseparable, we use MLP. Here what is meant b "data"--is it the response or the input feature that is linearly inseparable?
2) If it is linearly inseparable then does it mean that the mapping function from input to output will always be non-linear? Hence, we prefer MLP or the latest new models such as deep learning?
3) Linear regression fails in the case of linearly inseparable data or can linear regression work for inseparable data but if the function mapping is nonlinear then it fails?