Naive Bayes is a data science algorithm. It has the word ‘Bayes’ in it because it is based on the Bayes theorem, which deals with the probability of an event occurring given that another event has already occurred. It has ‘naive’ in it because it makes the assumption that each variab...
29. Why is Naive Bayes called ‘naive’? Naive Bayes is called naive because it makes the general assumption that all the data present are unequivocally important and independent of each other. This is not true and won’t hold up in a real-world scenario. 30. What is the simple differenc...
How would you improve a spam detection algorithm that uses naive Bayes? Have you been working with white lists? Positive rules? (In the context of fraud or spam detection) What is star schema? Lookup tables? Can you perform logistic regression with Excel? (yes) How? (use linest on log-t...
Application of Naive Bayes dichotomizer supported with expected risk and discriminant functions in clinical decisions — Case study In this paper, a case study on the application of Nai虉ve Bayes dichotomizer in clinical decision supporting systems is described. The case study is about ... A Prat...
naive_bayes import GaussianNB from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import StackingClassifier from sklearn.metrics import accuracy_score # Assuming X, y are your features and target respectively # Split data into train and test sets X_train, X_test, y_train, y...
The algorithms which uses OVO are: 1) Extreme learning Machines(ELM's) 2) Support Vector Machine(Classifiers) 3) K Nearest Neighbours.(for neighbouring classes based on distances) 4) Naive Bayes (based on MAP : Maximum Posterior ) 5) Decision Trees(decision in subnodes after parent node has...
Q: Why is Naive Bayes so bad? How would you improve a spam detection algorithm that uses naive Bayes? One major drawback of Naive Bayes is that it holds a strong assumption in that the features are assumed to be uncorrelated with one another, which typically is never the case. ...
Bayes’ Theorem is the basis behind a branch of machine learning that most notably includes theNaive Bayes classifier. That’s something important to consider when you’re faced with machine learning interview questions. Q7- Why is “Naive” Bayes naive?
18. Discuss ‘Naive’ in a Naive Bayes algorithm? It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence ...
This theorem is also the foundation behind the Machine Learning brand, which involves the Naive Bayes classifier. 10. What is PCA in Machine Learning? Principal Component Analysis, or PCA, is an unsupervised machine learning method widely used for dimensionality reduction. Its primary objective is ...