Concept Interview Questions

Bhavya V. - "Overfitting is the condition where your model is giving an unexpectedly higher accuracy because of its training in a small database and not getting exposed to anu different type of database while testing"See full answer

Shubhangi S. - "sanity testing, black box testing , white box testing, smoke testing,performance testing"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

Kavithadevi P. - "hash maps work in key value pair. The keys are hashed with a hash algorithm and resulting hashcode(integer) with related value are stored. Accessing a value, removing an element, Searching the hash map: 1) The hash map value can be accessed in O(1) time once you know the key. 2) If the key is not known, the hashmap value can be accessed in O(n) since you have to iterate atleast once. "See full answer

Harsh S. - "AUC 0.5 equates to a random model, so when creating any machine learning model or statistical model, you ideally want your model to at least beat this random baseline."See full answer

Archna M. - "Caching is a strategy to have the frequently accessed data in the memory to reduce the latency. whenever a client request the data from server, it is first accessed into cache and if not available then it is getting fetched from the storage and stored into cache. As cache is having limited memory, so amount of data can be stored in cache is less. Data will be flushed out from cache based on a criteria which is termed as caching strategy. There could be different mechanisms under which a data can"See full answer

Jake M. - "Random Forest is a machine learning model used for classification problems or regression problems. It can handle binary classification as well as multi-class classification. It is a very efficient model and is great for a baseline or used in a service that needs extremely low latency depending on the size of the model. It's also a good option for wide datasets (dataset with many features) due to it's random subset of features. it is slightly less optimized for deep datasets on very large dataset"See full answer