Concept Interview Questions

Lash - "A perceptron is the most basic building block of a neural network and represents a single-layer binary classifier."See full answer

1036 loknadh R. - "No ,MSE is suitable for only regression modes. Although the logistic regression in Its name has regression , but it is a classification problem so MSE is not suitable for classification models like logistic regression."See full answer

Matt M. - "Given the dataset does not contain many labels, it implies we cannot directly use supervised learning. I would ask more about the type of dataset we are given. Is it images, text, etc? This may inform the types of transformations we do the dataset. I can see two approaches to training Given the labels we do have, we can find a method to generate labels for the other unlabeled data. This likely will introduce some error since they may not be true labels, but it at least allows processing the"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

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

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