Technical Interview Questions
In pattern recognition, The information retrieval and classification in machine learning are part of precision. It is also called as positive predictive value which is the fraction of relevant instances among the retrieved instances. Recall is also known as sensitivity and the fraction of the total amount of relevant instances which were actually retrieved. Both … Read more
Selection bias stands for the bias which was introduced by the selection of individuals, groups or data for doing analysis in a way that the proper randomization is not achieved. It ensures that the sample obtained is not representative of the population intended to be analyzed and sometimes it is referred to as the selection … Read more
First, Naive Bayes is not one algorithm but a family of Algorithms that inherits the following attributes: 1.Discriminant Functions 2.Probabilistic Generative Models 3.Bayesian Theorem 4.Naive Assumptions of Independence and Equal Importance of feature vectors. Moreover, it is a special type of Supervised Learning algorithm that could do simultaneous multi-class predictions (as depicted by standing topics … Read more
Some of real world examples are as given below To mark an email as spam, or not spam? Classify a news article about technology, politics, or sports? Check a piece of text expressing positive emotions, or negative emotions? Also used for face recognition software In a machine learning interview, when asked about real-world applications of … Read more
Naive Bayes assumes conditional independence, P(X|Y, Z)=P(X|Z) P(X|Y,Z)=P(X|Z) P(X|Y,Z)=P(X|Z), Whereas more general Bayes Nets (sometimes called Bayesian Belief Networks), will allow the user to specify which attributes are, in fact, conditionally independent. For the Bayesian network as a classifier, the features are selected based on some scoring functions like Bayesian scoring function and minimal description … Read more