Interview Questions

Jyoti V. - "Over-fitting of a model occurs when model fails to generalize to any new data and has high variance withing training data whereas in under fitting model isn't able to uncover the underlying pattern in the training data and high bias. Tree based model like decision tree and random forest are likely to overfit whereas linear models like linear regression and logistic regression tends to under fit. There are many reasons why a Random forest can overfits easily 1. Model has grown to its full depth a"See full answer

Rahul M. - "We should define automated Scheduled Updates for deployment to keep the application up to date."See full answer

The_ A. - "The best and average of both the algorithms is same which is O(nlog(n)), but the worst time complexity of QuickSort is O(n^2){a case where all the elements are sorted in opposite to the fashion/order you want} while the worst TC for merge sort remains the same O(nlog(n)). and The SC for QS=O(logn) and MS=O(n)."See full answer

Sri V. - "Switching from a linear kernel to RBF / Gaussian kernel is likely to result in overfitting the model. It is a move that adds complexity to the mix, and if the data doesn't need that sort of complexity, it would result in overfitting. On the other hand, all the other three approaches would only try too reduce complexity in the process, thereby doesn't contribute to overfitting the model."See full answer