Technical Interview Questions
Bayes’ Theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if cancer is related to age, then, using Bayes’ theorem, a person’s age can be used to more accurately assess the probability that they have cancer than can be done without the … Read more
Scaling should be done post-train and test split ideally. If the data is closely packed, then scaling post or pre-split should not make much difference. The correct approach is to perform scaling after the train-test split. Here’s why: Information Leakage Prevention: Scaling before splitting may lead to information leakage from the test set to the … Read more
Instance Based Learning is a set of procedures for regression and classification which produce a class label prediction based on resemblance to its nearest neighbors in the training data set. These algorithms just collects all the data and get an answer when required or queried. In simple words they are a set of procedures for … Read more
Inductive Bias is a set of assumptions that humans use to predict outputs given inputs that the learning algorithm has not encountered yet. When we are trying to learn Y from X and the hypothesis space for Y is infinite, we need to reduce the scope by our beliefs/assumptions about the hypothesis space which is … Read more
Linear separability in feature space doesn’t imply linear separability in input space. So, Inputs are non-linearly transformed using vectors of basic functions with increased dimensionality. Limitations of Fixed basis functions are: Non-Linear transformations cannot remove overlap between two classes but they can increase overlap. Often it is not clear which basis functions are the best … Read more